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.

Monitoring and modelling surface deformation are crucial components of understanding the freeze-thaw process and preventing disasters in permafrost regions. However, previous methods had limitations that inhibited the interpretation of freeze-thaw deformation, such as a lack of physical meaning, an inability to reflect the physical freeze-thaw process and consideration of only a single external factor's impact on permafrost deformation. This study proposes an improved degree-day model (IDM) for quantitatively isolating surface deformation using interferometric synthetic aperture radar (InSAR) technology over permafrost. We considered the effect of soil moisture variation on permafrost deformation and incorporated interannual variation in the freeze-thaw process due to climate change. By applying small baseline subset (SBAS) technology to Sentinel-1 InSAR measurements over the Wudaoliang permafrost region on the Qinghai-Tibet Plateau from 2018 to 2019, we estimated long-term and seasonal permafrost deformation. The reliability of InSAR results was validated using in situ measurements, with root mean square errors (RMSEs) less than 10 mm. The results showed that the average linear deformation rates in 2018 and 2019 were -3.8 mm a-1 and -11.0 mm a-1, respectively, and the maximum seasonal deformations were 15.7 mm and 13.2 mm, respectively. Compared with the original degree-day model (ODM), the method used in this study produced smaller residual deformations of 6.9 mm and 6.4 mm, highlighting its ability to improve a quantitative description of permafrost deformation.

Winter baseflow (WB) can stabilize freshwater inputs and has important impacts on nutrient migration and the water cycle of a specific region and the oceans. This study systematically analyzed the WB variations in fourteen major Eurasian rivers and found they all had commonly increasing trends (except the Yellow River), with the mean increase ratio of 53.0% (+/- 34.8%, confidence interval 95%) over the past 100 years (the longest time series is 1879-2015). Relative to Northern Eurasia (60 degrees N-70 degrees N) and Southern Eurasia (30 degrees N-40 degrees N), the river WB in middle Eurasia (40 degrees N-60 degrees N) had the largest increase rate (0.60%/year). The increases of the WB in Northern Eurasia and Southern Eurasia have speeded up since the 1990s; on the contrary, they have slowed down or even turned to a decreasing trend after the 1990s in the middle Eurasian rivers. Using multiple linear regression analysis, the quantitative relationship between WB and winter surface air temperature (max, mean and min), snowfall, soil temperature, antecedent precipitation, as well as the river-ice dynamic were determined. We found that the winter air temperature, especially the minimum air temperature was one major factor accounting for WB variation in Eurasia over the past century. When the winter air temperature rises, this leads a reduction in the thickness and volume of river ice, and thus decreases water storage in river ice and leads to an increase in the WB. About 19.6% (6.7%-41.5%) of the winter WB increase in rivers of Siberia was caused by the decreased river ice during the past 100 years. Although groundwater recharge was the dominant reason for WB change, the role of river ice should not be ignored in hydrological study of cold regions.

China experiences severe particulate matter (PM) pollution. Although a monitoring network for PM2.5 (diameter < 2.5 mu m) has been set up in more than 100 major Chinese cities, insufficient spatial coverage of observations limits the study of the temporal and spatial characteristics, influencing factors, and component of PM2.5. In this study, we conducted a one year air quality simulation using a regional climate-chemistry model and evaluated the simulation's performance based on in situ observations concerning meteorological elements and PM2.5 concentrations. The simulated results showed that, higher PM2.5 concentrations appeared in northern China and the Sichuan Basin, and the maximal value occurred in winter. Furthermore, Vertical PM2.5 concentrations presented a gradual decreasing trend from the surface, whereas in southern coastal cities the profiles were unsteady with a secondary peak in the lower layer. Meteorological conditions were conducive to both pollutant diffusion and removal in summer, whereas stagnant conditions appeared in winter, characterized by high sea level pressure (SLP), the lowest planetary boundary layer height (PBLH), and 2-m temperature (T2). In provincial capital cities, PM2.5 was positively correlated with residential emissions but negatively correlated with precipitation, 10-m wind speed, T2, PBLH, and industrial emissions. Finally, we utilized the simulation results to investigate the component variations of PM2.5. Results indicated that primary PM2.5 components had significantly higher concentrations in northern China where residential heating is the major source of PM2.5 emissions, whereas they had lower concentrations in southern China. Secondary components played a crucial role in PM2.5 mass in eastern China. This study provided a clear perspective of seasonal variations, horizontal and vertical distributions of PM2.5 and its components and influence factors, which could be used in subsequent studies to investigate the formation mechanism and emission sources of PM2.5.

Empirical orthogonal function (EOF) and correlation analyses were employed to investigate the winter and spring snow depth in Eurasia and its relationship with Eastern China precipitation based on the observed and reanalyzed data from 1980 to 2016. The results show that the winter and spring snow cover in Eurasia not only highlights a decreasing trend due to global warming (the first EOF mode, its variance accounted for 24.4% and 22.6% of the total variance) but also exhibits notable interdecadal variation (the second EOF mode, its variance accounted for 10.2% and 11.5% of the total variance). The second EOF mode of winter snow depth in Eurasia is characterized by a west-east dipole pattern. It was observed that the spatial correlation pattern between the EOF2 of Eurasian snow depth and summer precipitation in China closely resembles the meridional quadrupole structure of the third EOF mode of summer precipitation in China. This pattern is characterized by excessive rainfall in Northeast China and the lower-middle reaches of the Yangtze River, and less rainfall over the Yellow River basin and southern China. The EOF mode of spring snow depth not only reflects the declining trend but also regulates precipitation in Eastern China. The possible mechanisms by which snow depth causes changes in soil moisture and subsequently affects atmospheric circulation are then explored from the perspective of the hydrological effects of snow cover. Decreased (Increased) snow depth in Eurasia during the winter and spring directly leads to diminished (increased) soil moisture while increasing (decreasing) net radiation and sensible heat flux at the surface. The meridional distribution of surface temperature also exhibits a dipole pattern, leading to enhanced subtropical westerly jet in the upper troposphere. The Eurasian snow cover anomalies pattern triggered an anomalous mid-latitude Eurasian wave train, which strengthened significantly in the Western Siberian Plain. It then splits into two branches, one continuing to propagate eastward at high latitudes and the other shifting towards East Asia, thereby impacting precipitation in Eastern China. This work indicates that the second EOF mode of Eurasian snow cover can impact the precipitation variability in Eastern China during the same period and in summer on an interdecadal scale.

Study region: The Urumqi River basin located in eastern Tien Shan in Central Aisa Study focus: Glacier runoff plays a pivotal role in water resources and stabilizing streamflow in mountainous regions. To assess the characteristics of glacier ice melt runoff in sub-basins within a single basin, three sub-basins with glacier ratios varying from 4% to 46% in the Urumqi River basin are investigated. Through the simulation by HBV light model on the basis of the observed meteorological and hydrological data. The characteristics and behaviour of glacier ice melt runoff in the three sub-basins are analysed. New hydrological insights for the region: It was found that both the contribution ratios of ice melt runoff and glacier runoff increase linearly with the increasing glacier ratio for the three catchments, rather than logarithmically or exponentially as observed in previous studies. This is due to the relatively high contributions of ice melt and glacier runoff to river flow in a catchment characterized by high elevation and extensive glacier coverage (Catchment 1), resulting from the coincidence of summer precipitation maxima with snow and ice melt in this region. The coefficient of variations (CV) of river flow tends to decrease with the decreasing glacier ratio in subbasins in the Urumqi River basin, indicating that river flow becomes more stable as it flows farther from the headwater in the Urumqi River basin. The lowest glacierized Catchment 3 exhibited the minimum CV value, demonstrating a stable outflow.

To address data scarcity on long-term glacial discharge and inadequacies in simulating and predicting hydrological processes in the Tien Shan, this study analysed the observed discharge at multiple timescales over 1980s-2017 and projected changes within a representative glacierized high-mountain region: eastern Tien Shan, Central Asia. Hydrological processes were simulated to predict changes under four future scenarios (SSP1, SSP2, SSP3, and SSP5) using a classical hydrological model coupled with a glacier dynamics module. Discharge rates at annual, monthly (June, July, August) and daily timescales were obtained from two hydrological gauges: Urumqi Glacier No.1 hydrological station (UGH) and Zongkong station (ZK). Overall, annual and summer discharge increased significantly ( p < 0.05) at both stations over the study period. Their intra-annual variations mainly resulted from differences in their recharge mechanisms. The simulations show that a tipping point in annual discharge at UGH may occur between 2018 and 2024 under the four SSPs scenarios. Glacial discharge is predicted to cease earlier at ZK than at UGH. This relates to glacier type and size, suggesting basins with heavily developed small glaciers will reach peak discharge sooner, resulting in an earlier freshwater supply challenge. These findings serve as a reference for research into glacial runoff in Central Asia and provide a decision-making basis for planning local water-resource projects.

The freezing index (FI) is one of the most important indicators that shows the variation of permafrost. However, the relationship between climate change and the thermal conditions of permafrost is not understood well. This study analyzed the variation of FI based on 5-cm soil temperature derived from 74 meteorological stations from 1977 to 2016 on the Qinghai-Tibet Plateau (QTP). Furthermore, the factors affecting the FI variation and its relationship with permafrost degradation were also discussed. The results showed that FI was much smaller in the interior than other areas of the QTP, and it increased at a rate of 53.0 degrees C d/10a during the 40 years. FI in the main body of the QTP was relatively stable than surrounding areas; it was more stable in the northern part than in the southern part. On average, the FI variation coefficient was larger than 10%, indicating the large fluctuation of FI during the 40 years. FI decreased with the increasing altitude; it was more sensitive to the altitude in the south of 33 degrees N than in the north. The variation of FI was closely related to the maximum freezing depth (MFD) and the active layer thickness (ALT). It was observed that MFD decreased and ALT increased by approximately 1.4 cm and 1.6 cm, respectively, with each 10.0 degrees C d increase in FI. The results exhibited the thermal condition variation of the permafrost in QTP and revealed a degrading trend of the permafrost.

High latitude regions are experiencing considerable winter climate change, and reduced snowpack will likely affect soil microbial communities and their function, ultimately altering microbial-mediated biogeochemical cycles. However, the current knowledge on the responses of soil microorganisms to snow cover changes in permafrost ecosystems remains limited. Here, we conducted a 2-year (six periods) snow manipulation experi-ment comprising ambient snow and snow removal treatments with three replications of each treatment to explore the immediate and legacy effects of snow removal on soil bacterial community and enzyme activity in secondary Betala platyphylla forests in the permafrost region of the Daxing'an Mountains. Generally, bacterial community diversity was not particularly sensitive to the snow removal. Seasonal fluctuations in the relative abundance of dominated bacterial taxa were observed, but snow removal merely exerted a significant impact on the bacterial community structure during the snow melting period and early vegetation growing season within two consecutive years, with a reduction in the relative abundance of Chloroflexi and an increase in the relative abundance of Actinobacteria, and no evidence of cross-season legacy effects was found. Moreover, snow removal significantly altered the soil enzyme activities in the snow stabilization period and snow melting period, with an increase in soil acid phosphatase (ACP) activity of snow melting period and a decrease in polyphenol oxidase (PPO) activity of snow stabilization period as well as beta-glucosidase (BG) activity of snow stabilization period and snow melting period, but this effect did not persist into the vegetation growing periods. The seasonal variations in bacterial community and enzyme activity were mostly driven by changes in soil nutrient availability. Overall, our results suggest that soil bacterial communities have rather high resilience and rapid adaptability to snow cover changes in the forest ecosystems in the cold region of the Daxing'an Mountains.

Permafrost is a potential mercury (Hg) pool released by thawing, which can raise the risk of Hg pollution under global warming. Tree rings are useful archives of environment-specific Hg exposure over long periods. We determined Hg concentrations in tree rings of two dominant tree species (Larix gmelinii Rupr. and Pinus sylvestris var. mongolica) at permafrost sites in northeastern China. The biweighted mean Hg concentrations ranged from 0.36 to 3.96 ng g(-1) from 1840 to 2014. The tree-ring width had no significant influence on the Hg concentration. Larch Hg increased slightly before the 1970s and peaked in the 1990s. However, the pine Hg concentration increased continuously until the 1930s, decreased rapidly until the 1970s, then rose to a peak in the late 1980s. The change of Hg concentrations in larch and pine revealed a time offset of 4 to 5 years, which implied possibly high mobility of Hg in pine tree rings. Higher Hg concentrations from 1920 to 1960 and subsequent decreases in isolated permafrost forests revealed the local geographical Hg cycling history. Lower Hg concentrations and faster increases in larch suggest the role of additional winter Hg loading for the evergreen pine and species-specific differences in root absorption in response to melting permafrost. Our results highlight possible geographical impacts on tree-ring Hg records, improve understanding of Hg cycles in permafrost forest, and suggest a need to sample additional species in a range of permafrost environments.