Surface soil moisture (SSM) is a key limiting factor for vegetation growth in alpine meadow on the Qinghai-Tibetan Plateau (QTP). Patches with various sizes and types may cause the redistribution of SSM by changing soil hydrological processes, and then trigger or accelerate alpine grassland degradation. Therefore, it is vital to understand the effects of patchiness on SSM at multi-scales to provide a reference for alpine grassland restoration. However, there is a lack of direct observational evidence concerning the role of the size and type of patches on SSM, and little is known about the effects of patches pattern on SSM at plot scale. Here, we first measured SSM of typical patches with different sizes and types at patch scale and investigated their patterns and SSM spatial distribution through unmanned aerial vehicle (UAV)-mounted multi-type cameras at plot scale. We then analyzed the role of the size and type of patchiness on SSM at both patch and plot scales. Results showed that: (1) in situ measured SSM of typical patches was significantly different (P < 0.01), original vegetation patch (OV) had the highest SSM, followed by isolate vegetation patch (IV), small bare patch (SP), medium bare patch (MP) and large bare patch (LP); (2) the proposed method based on UAV images was able to estimate SSM (0-40 cm) with a satisfactory accuracy (R-2 = 0.89, P < 0.001); (3) all landscape indices of OV, with the exception of patch density, were positively correlated with SSM at plot scale, while most of the landscape indices of LP and IV showed negative correlations (P < 0.05). Our results indicated that patchiness intensified the spatial heterogeneity of SSM and potentially accelerated the alpine meadow degradation. Preventing the development of OV into IV and the expansion of LP is a critical task for alpine meadow management and restoration.

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.

Vast deserts and sandy lands in the mid-latitudes cover an area of 17.64 x 106 km2, with 6.98 x 106 km2 experiencing seasonal frozen soil (SFG). Freeze-thaw cycles of SFG significantly influence local surface processes in deserts, impacting meteorological disasters such as infrastructure failures and sandstorms. This study investigates the freeze-thaw dynamics of SFG in crescent dunes from three deserts in northern China: the Tengger Desert, Mu Us Sandy Land, and Ulan Buh Desert, over the period from 2019 to 2024.Freezing occurs from November to January, followed by thawing from January to March. The thawing rate (2.72 cm/day) was 1.8 times higher than the freezing rate (1.48 cm/day). The maximum seasonal freezing depth (MSFD) exceeded 0.80 mat all dune slopes, with depths surpassing 1.10 mat the leeward slope and lower slope positions. Soil moisture content, ranging from 1 % to 1.6 %, is critical for freezing, and this threshold varies depending on the dune's mechanical composition. The hardness of frozen desert soil is primarily controlled by moisture, along with temperature and particle size.Temperature initiates freezing, while moisture and particle size control the resulting hardness.These findings shed light on the seasonal freeze-thaw processes in desert soils and have practical implications for agricultural management, engineering design, and environmental hazard mitigation in arid regions.

Soil freeze-thaw state influences multiple terrestrial ecosystem processes, such as soil hydrology and carbon cycling. However, knowledge of historical long-term changes in the timing, duration, and temperature of freeze-thaw processes remains insufficient, and studies exploring the combined or individual contributions of climatic factors-such as air temperature, precipitation, snow depth, and wind speed-are rare, particularly in current thermokarst landscapes induced by abrupt permafrost thawing. Based on ERA5-Land reanalysis, MODIS observations, and integrated thermokarst landform maps, we found that: 1) Hourly soil temperature from the reanalysis effectively captured the temporal variations of in-situ observations, with Pearson' r of 0.66-0.91. 2) Despite an insignificant decrease in daily freeze-thaw cycles in 1981-2022, other indicators in the Qinghai-Tibet Plateau (QTP) changed significantly, including delayed freezing onset (0.113 d yr- 1), advanced thawing onset (-0.22 d yr- 1), reduced frozen days (-0.365 d yr- 1), increased frozen temperature (0.014 degrees C yr- 1), and decreased daily freeze-thaw temperature range (-0.015 degrees C yr- 1). 3) Total contributions indicated air temperature was the dominant climatic driver of these changes, while indicators characterizing daily freeze-thaw cycles were influenced mainly by the combined effects of increased precipitation and air temperature, with remarkable spatial heterogeneity. 4) When regionally averaged, completely thawed days increased faster in the thermokarstaffected areas than in their primarily distributed grasslands-alpine steppe (47.69%) and alpine meadow (22.64%)-likely because of their stronger warming effect of precipitation. Locally, paired comparison within 3 x 3 pixel windows from MODIS data revealed consistent results, which were pronounced when the thermokarst-affected area exceeded about 38% per 1 km2. Conclusively, the warming and wetting climate has significantly altered soil freeze-thaw processes on the QTP, with the frozen soil environment in thermokarstaffected areas, dominated by thermokarst lakes, undergoing more rapid degradation. These insights are crucial for predicting freeze-thaw dynamics and assessing their ecological impacts on alpine grasslands.

Air pollution is a global health issue, and events like forest fires, agricultural burning, dust storms, and fireworks can significantly worsen it. Festivals involving fireworks and wood-log fires, such as Diwali and Holi, are key examples of events that impact local air quality. During Holi, the ritual of Holika involves burning of biomass that releases large amounts of aerosols and other pollutants. To assess the impact of Holika burning, observations were conducted from March 5th to March 18th, 2017. On March 12th, 2017, around 1.8 million kg of wood and biomass were openly burned in about 2250 units of Holika, located in and around the Varanasi city (25.23 N, 82.97 E, similar to 82.20 m amsl). As the Holika burning event began the impact on the Black Carbon (BC), particulate matter 10 & 2.5 (PM10 and PM2.5), sulphur dioxide (SO2), oxides of nitrogen (NOx), ozone (O-3) and carbon monoxide (CO) concentration were observed. Thorough optical investigations have been conducted to better comprehend the radiative effects of aerosols produced due to Holika burning on the environment. The measured AOD at 500 nm values were 0.315 +/- 0.072, 0.392, and 0.329 +/- 0.037, while the BC mass was 7.09 +/- 1.78, 9.95, and 7.18 +/- 0.27 mu g/m(3) for the pre-Holika, Holika, and post-Holika periods. Aerosol radiative forcing at the top of the atmosphere (ARF-TOA), at the surface (ARF-SUR), and in the atmosphere (ARF-ATM) are 2.46 +/- 4.15, -40.22 +/- 2.35, and 42.68 +/- 4.12 W/m(2) for pre-Holika, 6.34, -53.45, and 59.80 W/m(2) for Holika, and 5.50 +/- 0.97, -47.11 +/- 5.20, and 52.61 +/- 6.17 W/m(2) for post-Holika burning. These intense observation and analysis revealed that Holika burning adversely impacts AQI, BC concentration and effects climate in terms of ARF and heating rate.

Ongoing climate warming and increased human activities have led to significant permafrost degradation on the Qinghai-Tibet Plateau (QTP). Mapping the distribution of active layer thickness (ALT) can provide essential information for understanding this degradation. Over the past decade, InSAR (Interferometric synthetic aperture radar) technology has been utilized to estimate ALT based on remotely-sensed surface deformation information. However, these methods are generally limited by their ability to accurate extract seasonal deformation and model subsurface water content of active layer. In this paper, an ALT inversion method considering both seasonal deformation from InSAR and smoothly multilayer soil moisture from ERA5 is proposed. Firstly, we introduce a ground seasonal deformation extraction model combining RobustSTL and InSAR, and the deformation extraction accuracy by considering the deformation characteristics of permafrost are evaluated, proving the effectiveness of RobustSTL in extracting seasonal deformation of permafrost. Then, using ERA5 soil moisture products, a smoothed multilayer soil moisture model for ALT inversion is established. Finally, integrating the seasonal deformation and multilayer soil moisture, the ALT can be estimated. The proposed model is applied to the Yellow River source region (YRSR) with Sentinel-1A images acquired from 2017 to 2021, and the ALT retrieval accuracy is validated with measured data. Experimental results show that the vertical deformation rate of the study area generally ranges from -30 mm/year to 20 mm/year, with seasonal deformation amplitude ranging from 2 mm to 30 mm. The RobustSTL method has the highest accuracy in extracting seasonal deformation of permafrost, with an RMSE (root mean square error) of 0.69 mm, and is capable of capturing the freeze-thaw characteristics of the active layer. The estimated ALT of the YRSR ranges from 49 cm to 450 cm, with an average value of 145 cm. Compared to the measured data, the proposed method has an average error of 37.5 cm, which represents a 21 % improvement in accuracy over existing methods.

The Atacama Plateau in the Central Andes (28-22 degrees S) is characterised by a dry and cold periglacial tundra due to the high altitude, low precipitation, and high evaporation. Endogenous freshwater sources - e.g.: seasonal streams and lakes, subsurface reservoirs, surface snow/ice patches - are available, though they are highly sensitive to climatic changes. The near surface hydrological network is highly modified by the distribution and seasonal evolution of perennial frozen ground, i.e. permafrost, which is also expected to change in the future. The interplay between permafrost and hydrology, especially in relation to future climate change, is poorly explored. To address this issue, we carry out long-term ground temperature measurement and modelling, snow coverage survey, tritium- and stable isotope analysis of surface waters on the Ojos del Salado Massif, which is representative of high altitude mountains on the Atacama Plateau. According to our results, a highly transient surface hydrological network - lakes, springs and streams - forms during each summer where permafrost is widespread and ground thawing (i.e. active layer) is present (similar to 4900-6500 m a.s.l.). In this system, the water is of meteoric origin and relatively young (<10 years). The development of the network is strongly influenced by the active layer, which plays a crucial role in storing, seeping, and discharging groundwater. However, future permafrost degradation is expected to reduce the seasonal presence of shallow water, and hence, modify groundwater recharge patterns.

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.

Climate change impacts water supply dynamics in the Upper Rio Grande (URG) watersheds of the US Southwest, where declining snowpack and altered snowmelt patterns have been observed. While temperature and precipitation effects on streamflow often receive the primary focus, other hydroclimate variables may provide more specific insight into runoff processes, especially at regional scales and in mountainous terrain where snowpack is a dominant water storage. The study addresses the gap by examining the mechanisms of generating streamflow through multi-modal inferences, coupling the Bayesian Information Criterion (BIC) and Bayesian Model Averaging (BMA) techniques. We identified significant streamflow predictors, exploring their relative influences over time and space across the URG watersheds. Additionally, the study compared the BIC-BMA-based regression model with Random Forest Regression (RFR), an ensemble Machine Learning (RFML) model, and validated them against unseen data. The study analyzed seasonal and long-term changes in streamflow generation mechanisms and identified emergent variables that influence streamflow. Moreover, monthly time series simulations assessed the overall prediction accuracy of the models. We evaluated the significance of the predictor variables in the proposed model and used the Gini feature importance within RFML to understand better the factors driving the influences. Results revealed that the hydroclimate drivers of streamflow exhibited temporal and spatial variability with significant lag effects. The findings also highlighted the diminishing influence of snow parameters (i. e., snow cover, snow depth, snow albedo) on streamflow while increasing soil moisture influence, particularly in downstream areas moving towards upstream or elevated watersheds. The evolving dynamics of snowmelt-runoff hydrology in this mountainous environment suggest a potential shift in streamflow generation pathways. The study contributes to the broader effort to elucidate the complex interplay between hydroclimate variables and streamflow dynamics, aiding in informed water resource management decisions.

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.

Glaciers playa vital role in providing water resources for drinking, agriculture, and hydro-electricity in many mountainous regions. As global warming progresses, accurately reconstructing long-term glacier mass changes and comprehending their intricate dynamic relationships with environmental variables are imperative for sustaining livelihoods in these regions. This paper presents the use of eXplainable Machine Learning (XML) models with GRACE and GRACE-FO data to reconstruct long-term monthly glacier mass changes in the Upper Yukon Watershed (UYW), Canada. We utilized the H2O-AutoML regression tools to identify the best performing Machine Learning (ML) model for filling missing data and predicting glacier mass changes from hydroclimatic data. The most accurate predictive model in this study, the Gradient Boosting Machine, coupled with explanatory methods based on SHapley Additive eXplanation (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) analyses, led to automated XML models. The XML unveiled and ranked key predictors of glacier mass changes in the UYW, indicating a decrease since 2014. Analysis showed decreases in snow water equivalent, soil moisture storage, and albedo, along with increases in rainfall flux and air temperature were the main drivers of glacier mass loss. A probabilistic analysis hinging on these drivers suggested that the influence of the key hydrological features is more critical than the key meteorological features. Examination of climatic oscillations showed that high positive anomalies in sea surface temperature are correlated with rapid depletion in glacier mass and soil moisture, as identified by XML. Integrating H2OAutoML with SHAP and LIME not only achieved high prediction accuracy but also enhanced the explainability of the underlying hydroclimatic processes of glacier mass change reconstruction from GRACE and GRACE-FO data in the UYW. This automated XML framework is applicable globally, contingent upon sufficient high-quality data for model training and validation.