Global warming has led to permafrost degradation worldwide. The Qinghai‐Tibet Plateau (QTP) hosts most of the world's alpine permafrost, yet its impending changes remain largely unclear, thereby affecting regional hydrological and ecological processes and the global carbon budget. By employing a land surface model adapted to simulate frozen ground, and using state‐of‐the‐art multi‐model and multi‐scenario data from the Coupled Model Intercomparison Project Phase 6, changes in permafrost distribution and its thermal regimes on the QTP are systematically predicted under various shared socioeconomic pathways (SSPs). Projections for SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5 show that most of the continuous permafrost region of the QTP will persist through 2050. Much of the permafrost is likely to degrade in the late 21st century, with projected area losses of 44 ± 4%, 59 ± 5%, and 71 ± 7%, respectively, by 2100. In particular, the Three Rivers Source region in the central eastern part of the QTP is a key area of permafrost degradation, where permafrost is most vulnerable and degradation occurs earliest. The mean annual ground temperature of QTP permafrost will increase by 0.8 ± 0.2°C, 2.0 ± 0.3°C, and 2.6 ± 0.3°C under SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5, respectively, and the active layer thickness will increase by 0.7 ± 0.1 m, 1.5 ± 0.3 m, and 3.0 ± 1.0 m, respectively. The surviving permafrost under SSP3‐7.0 and SSP5‐8.5 will be thermally unstable, which is a clear warning sign of complete disappearance. The analysis of permafrost sensitivity to climate change signifies that alpine permafrost on the QTP has low resilience to climate change, in contrast to permafrost in pan‐Artic high latitudes.

Climate warming is thermally degrading permafrost. The climate warming over the Qinghai‐Tibet Plateau (QTP) is characterized by obvious seasonality and stronger winter warming in comparison with summer warming in 2000s. The contributions of seasonal climate warming to permafrost thermal responses remain unknown. Here, we isolated the seasonal warming contributions to permafrost thermal regimes through contrasting a series of specially designed numerical experiments that were composed of decadal seasonal data segments. The results reveal that permafrost thermal regime is highly sensitive to winter temperature change. Permafrost in the Three River Source region and on the Qiangtang High Plateau has undergone remarkable thermal degradation in the 2000s due to winter warming. It was estimated that each degree of rising in air temperature in summer could lead to an average increase of 0.35 m in the active layer thickness (ALT) and of 0.20°C in the mean annual ground temperature (MAGT), while for 1° winter warming, the active layer thickens by 0.16 m and the MAGT increases by 0.13°C. However, the overall impacts of winter warming have exceeded summer warming in 2000s due to a triple warming rate in winter than that in summer in this period.

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.

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.

The lack of direct observations for flood regulation capacity largely compromises our objective assessment of a basin's flood mitigation strategies in confronting global change. This study proposes a novel Precipitation‐Inundation Paired Events (PIPE) extraction frame‐work, which employs 3D clustering analyses of precipitation paired up with satellite‐based inundation maps, allowing for the spatiotemporal analyses of event‐based Inundation‐to‐Precipitation (IP) ratio. Results from 142 events in the Yangtze River Basin show that the proposed metric can effectively capture the natural controls on flooding, reflected in inundation responses to key precipitation characteristics such as its spatiotemporal distribution and trajectories. Notably, after the operation of the Three Gorges Dam, the median IP Ratio for moderate and long‐duration events declined significantly, representing a ∼15% reduction in inundation area per unit precipitation volume. This study offers a novel tool for quantifying the spatiotemporal dynamics of a basin's flood regulation capacity and guiding flood management studies.