Ice records provide a qualitative rather than a quantitative indication of the trend of climate change. Using the bulk aerodynamic method and degree day model, this study quantified ice mass loss attributable to sublimation/evaporation (S/E) and meltwater on the basis of integrated observations (1960-2006) of glacier-related and atmospheric variables in the northeastern Tibetan Plateau. During 1961-2005, the average annual mass loss in the ice core was 95.33 +/- 20.56 mm w.e. (minimum: 78.97 mm w.e. in 1967, maximum: 146.67 mm w.e. in 2001), while the average ratio of the revised annual ice accumulation was 21.2 +/- 7.7% (minimum: 11.0% in 1992, maximum 44.8% in 2000). A quantitative formula expressing the relationship between S/E and air temperature at the monthly scale was established, which could be extended to estimation of S/E changes of other glaciers in other regions. The elevation effect on alpine precipitation determined using revised ice accumulation and instrumental data was found remarkable. This work established a method for quantitative assessment of the temporal variation in ice core mass loss, and advanced the reconstruction of long-term precipitation at high elevations. Importantly, the formula established for reconstruction of S/E from temperature time series data could be used in other regions.

Alpine wet meadow (AWM), an important wetland type on the Qinghai-Tibet Plateau (QTP), is sensitive to climate change, which alters the soil hydrothermal regime and impacts ecological and hydrological functions in permafrost regions. The mechanisms underlying extreme AWM degradation in the QTP and hydrothermal factors controlling permafrost degradation remain unclear. In this study, soil hydrothermal processes, soil heat migration, and the permafrost state were measured in AWM and extremely degraded AWM (EDAWM). The results showed that the EDAWM exhibited delayed onset of both soil thawing and freezing, shortened thawing period, and extended freezing period at the lower boundary of the active layer. The lower ground temperatures resulted in a 0.2 m shallower active layer thickness in the EDAWM compared with the AWM. Moreover, the EDAWM altered soil thermal dynamics by redistributing energy, modifying soil moisture, preserving soil organic matter, and adjusting soil thermal properties. As for energy budget, a substantial amount of heat in the EDAWM was consumed by turbulent heat fluxes, particularly latent heat flux, which reduced the amount of heat transferred to the ground. Additionally, the higher soil organic matter content in EDAWM decreased the annual mean soil thermal conductivity from 1.42 W m- 1 K-1 in AWM to 1.26 W m- 1 K-1 in EDAWM, slowing down heat transfer within the active layer and consequently mitigating permafrost degradation. However, with continued climate warming, the soil organic matter content in EDAWM will inevitably decline due to microbial decomposition in the absence of new organic inputs. As the soil organic matter content diminishes, soil heat transfer processes will likely accelerate, and the permafrost warming rate may surpass that in undistributed AWM. These findings enhance our understanding of how alpine ecosystem succession influences regional hydrological cycles and greenhouse gas emissions.

Lakes are commonly accepted as a sensitive indicator of regional climate change, including the Tibetan Plateau (TP). This study took the Ranwu Lake, located in the southeastern TP, as the research object to investigate the relationship between the lake and regional hydroclimatological regimes. The well-known Budyko framework was utilized to explore the relationship and its causes. The results showed air temperature, evapotranspiration and potential evapotranspiration in the Ranwu Lake Basin generally increased, while precipitation, soil moisture, and glacier area decreased. The Budyko space indicated that the basin experienced an obviously drying phase first, and then a slightly wetting phase. An overall increase in lake area appears inconsistent with the drying phase of the basin climate. The inconsistency is attributable to the significant expansion of proglacial lakes due to glacial melting, possibly driven by the Atlantic Multidecadal Oscillation. Our findings should be helpful for understanding the complicated relationships between lakes and climate, and beneficial to water resources management under changing climates, especially in glacier basins.

Numerous endorheic lakes in the Qinghai-Tibet Plateau (QTP) have shown a dramatic increase in total area since 1996. These expanding lakes are mainly located in the interior regions of the QTP, where permafrost is widely distributed. Despite significant permafrost degradation due to global warming, the impact of permafrost thawing on lake evolution in QTP has been underexplored. This study investigated the permafrost degradation and its correlation with lake area increase by selecting four lake basins (Selin Co, Nam Co, Zhari Namco, and Dangqiong Co) in QTP for analysis. Fluid-heat-ice coupled numerical models were conducted on the aquifer cross-sections in these four lake basins, to simulate permafrost thawing driven by rising surface temperatures, and calculate the subsequent changes in groundwater discharge into the lakes. The contribution of these changes to lake storage, which is proportional to lake area, was investigated. Numerical simulation indicates that from 1982 to 2011, permafrost degradation remained consistent across the four basins. During this period, the active layer thickness first increased, then decreased, and partially transformed into talik, with depths reaching up to 25 m. By 2011, groundwater discharge had significantly risen, exceeding 2.9 times the initial discharge in 1988 across all basins. This increased discharge now constitutes up to 17.67 % of the total lake water inflow (Selin Co). The dynamic lake water budget further suggests that groundwater contributed significantly to lake area expansion, particularly since 2000. These findings highlight the importance of considering permafrost thawing as a crucial factor in understanding the dynamics of lake systems in the QTP in the context of climate change.

The Qinghai-Tibetan Plateau (QTP) has undergone significant warming, wetting, and greening (WWG) over decades, alongside substantial alterations in hydrological regimes. These changes present great challenges for safeguarding water resources and ecosystems downstream. However, the lack of field observation and systematic research has obscured our understanding of how hydrological processes respond to the combined influences of climate-permafrost-vegetation. This study focuses on the source regions of the Yangtze River, one of the highest permafrost-covered basins on the QTP, and employs a process-based hydrological model to quantify the effects of WWG on hydrological processes. We show that the increasing precipitation dominates subsurface runoff changes while rising temperature primarily affects surface runoff changes by reducing the frozen duration (-52 days/century) and thickening the active layer (+2.4 cm/year). Greening vegetation primarily affects transpiration and interception evaporation. Warming, wetting, and greening will cause a transition in runoff dynamics from surface runoff dominance to subsurface runoff dominance in permafrost basins, and reduce the risk of both flooding and water shortage indicated by the decreased maximum low flow duration and maximum high flow duration of 11.0 and 5.0 days/year, respectively. Moreover, cold permafrost regions exhibit a greater propensity for generating runoff, as indicated by a higher annual increase in runoff coefficient (0.005/year) and total runoff (4.81 mm/year), compared to warm permafrost regions (with increase of 0.001/year and 1.20 mm/year, respectively). These findings enhance the understanding of hydrological changes due to WWG and provide insights for water resources management in permafrost regions under climate change.

Snow cover variation significantly impacts alpine vegetation dynamics on the Tibetan Plateau (TP), yet this effect under climate change remains underexplored. This study uses a survival analysis model to assess the influence of snow on vegetation green-up dynamics, while controlling for key temperature and water availability factors. This analysis integrates multi-source data, including satellite-derived vegetation green-up dates (GUDs), snow depth, accumulated growing degree days (AGDD), downward shortwave radiation (SRAD), precipitation, and soil moisture. Our survival analysis model effectively simulated GUD on the TP, achieving an R of 0.62 (p < 0.01), a root mean square error (RMSE) of 11.20 days, and a bias of -1.41 days for 2020 GUD predictions. It outperformed both the model excluding snow depth and a linear regression model. By isolating snow's impact, we found it exerts a stronger influence on vegetation GUD than precipitation in snow-covered areas of the TP. Furthermore, snow depth effects varied seasonally: a 1-cm increase in preseason snow depth reduced green-up rates by 8.48% before 156(th) day but increased them by 4.74% afterward. This indicates that deeper preseason snow cover delays GUD before June, but advances it from June onward, rather than having a uniform effect. These findings highlight the critical role of snow and underscore the need to incorporate its distinct effects into vegetation phenology models in alpine regions.

Numerical modeling of permafrost dynamics requires adequate representation of atmospheric and surface processes, a reasonable parameter estimation strategy, and site-specific model development. The three main research objectives of the study are: (i) to propose a novel methodology that determines the required level of surface process complexity of permafrost models by conducting parameter sensitivity and calibration, (ii) to design and compare three numerical models of increasing surface process complexity, and (iii) to calibrate and validate the numerical models at the Yakou catchment on the Qinghai-Tibet Plateau as an exemplary study site. The calibration was carried out by coupling the Advanced Terrestrial Simulator (numerical model) and PEST (calibration tool). Simulation results showed that (i) A simple numerical model that considers only subsurface processes can simulate active layer development with the same accuracy as other more complex models that include surface processes. (ii) Peat and mineral soil layer permeability, Van Genuchten alpha, and porosity are highly sensitive. (iii) Liquid precipitation aids in increasing the rate of permafrost degradation. (iv) Deposition of snow insulated the subsurface during the thaw initiation period. We have developed and released an integrated code that couples the numerical software ATS to the calibration software PEST. The numerical model can be further used to determine the impacts of climate change on permafrost degradation.

Glaciers provide multiple ecosystem services (ES) to human society. Due to the continued global warming, the valuation of glacier ES is of urgent importance because this knowledge can support the protection of glaciers. However, a systematic valuation of glacier ES is still lacking, particularly from the perspective of ES contributors. In this study, we introduce the concept of emergy to establish a methodological framework for accounting glacier ES values, and take the Tibetan Plateau (TP) as a case study to comprehensively evaluate the spatiotemporal characteristics of glacier ES during the early 21st century. The results show that the total glacier ES values on the TP increased from 2.36E+24 sej/yr in the 2000s to 2.40E+24 sej/yr in the 2010s, with an overall growth rate of 1.6%. The values of the various services in the 2010s are ranked in descending order: climate regulation (1.59E+24 sej/yr, 66.1%), runoff regulation (4.40E+23 sej/yr, 18.4%), hydropower generation (1.88E+23 sej/ yr, 7.8%). Significantly higher glacier ES values were recorded in the marginal TP than in the endorheic area. With the exception of climate regulation and carbon sequestration, all other service values increased during the study period, partially cultural services, which have experienced rapid growth in tandem with social development. The results of this study will help establish the methodological basis for the assessment of regional and global glacier ES, as well as a scientific basis for the regional protection of glacier resources.

In the context of global warming, landscapes with ice-rich permafrost, such as the Qinghai-Tibet Plateau (QTP), are highly vulnerable. The expansion of thermokarst lakes erodes the surrounding land, leading to collapses of various scales and posing a threat to nearby infrastructure and the environment. Assessing the susceptibility of thermokarst lakes in remote, data-scarce areas remains a challenging task. In this study, Landsat imagery and human-computer interaction were employed to improve the accuracy of thermokarst lake classification. The study also identified the key factors influencing the occurrence of thermokarst lakes, including the lake density, soil moisture (SM), slope, vegetation, snow cover, ground temperature, precipitation, and permafrost stability (PS). The results indicate that the most susceptible areas cover 19.02% of the QTP's permafrost region, primarily located in southwestern Qinghai, northeastern Tibet, and the Hoh Xil region. This study provides a framework for mapping the spatial distribution of thermokarst lakes and contributes to understanding the impact of climate change on the QTP.

As a key component of the cryosphere, permafrost is sensitive to climate change, but mapping permafrost, especially in the Tibetan Plateau, has been challenging due to the heterogeneous mountainous landscape and limited representativeness of ground observations. Using 155 compiled ground observations and more than 20,000 rock glacier records, we developed a machine learning model to map the distribution of permafrost and produce an improved permafrost zonation index (PZI) map. The model was applied by incorporating several control variables, including terrain (elevation and relief), soil (bulk density, clay, coarse fragments, sand, and silt), and temperature (MAAT, FDD, and TDDT) to estimate the PZI at a 1-km resolution in the southern Tibetan Plateau. Excluding glaciers and lakes, the area of permafrost estimated by the new map is approximately 103.5 x 103 km2, accounting for 47.8% of the total area of the region. The result was assessed with various datasets and compared with existing permafrost maps and achieved higher accuracy compared with previous studies. The overall classification accuracy was 96.1% in high plain areas and 84.4% in mountain areas. The results demonstrated the substantial potential for improving mapping permafrost and understanding the periglacial environment with rock glacier inventories and machine learning, especially in complex terrain and climate.