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.

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.

To understand the characteristics of particulate matter (PM) and other air pollutants in Xinjiang, a region with one of the largest sand-shifting deserts in the world and significant natural dust emissions, the concentrations of six air pollutants monitored in 16 cities were analyzed for the period January 2013-June 2019. The annual mean PM2.5, PM10, SO2, NO2, CO, and O-3 concentrations ranged from 51.44 to 59.54 mu g m(-3), 128.43-155.28 mu g m(-3), 10.99-17.99 mu g m(-3), 26.27-31.71 mu g m(-3), 1.04-1.32 mg m(-3), and 55.27-65.26 mu g m(-3), respectively. The highest PM concentrations were recorded in cities surrounding the Taklimakan Desert during the spring season and caused by higher amounts of wind-blown dust from the desert. Coarse PM (PM10-2.5) was predominant, particularly during the spring and summer seasons. The highest PM2.5/PM10 ratio was recorded in most cities during the winter months, indicating the influence of anthropogenic emissions in winters. The annual mean PM2.5 (PM10) concentrations in the study area exceeded the annual mean guidelines recommended by the World Health Organization (WHO) by a factor of ca. similar to 5-6 (similar to 7-8). Very high ambient PM concentrations were recorded during March 19-22, 2019, that gradually influenced the air quality across four different cities, with daily mean PM2.5 (PM10) concentrations similar to 8-54 (similar to 26-115) times higher than the WHO guidelines for daily mean concentrations, and the daily mean coarse PM concentration reaching 4.4 mg m(-3). Such high PM2.5 and concentrations pose a significant risk to public health. These findings call for the formulation of various policies and action plans, including controlling the land degradation and desertification and reducing the concentrations of PM and other air pollutants in the region. (C) 2020 Elsevier Ltd. All rights reserved.

The soil freeze-thaw phenomenon is one of the most outstanding characteristics of the soil in Heilongjiang Province. Quantitative analysis of the characteristics of changes in key variables of the soil freeze-thaw processes is of great scientific importance for understanding climate change, as well as ecological and hydrological processes. Based on the daily surface temperature and air temperature data in Heilongjiang Province for the past 50 years, the spatial-temporal distribution characteristics of key variables and their correlations with air temperature and latitude in the freeze-thaw process of soil were analyzed using linear regression, the Mann-Kendall test, the local thin disk smooth spline function interpolation method, and correlation analysis; additionally, the spatial-temporal distribution of key variables and the changes in the surface temperature during the freeze-thaw process are discussed under different vegetation types. The results show that there is a trend of delayed freezing and early melting of key variables of the soil freeze-thaw process from north to south. From 1971 to 2019 a, the freezing start date (FSD) was delayed at a rate of 1.66 d/10 a, the freezing end date (FED) advanced at a rate of 3.17 d/10 a, and the freezing days (FD) were shortened at a rate of 4.79 d/10 a; with each 1 degrees C increase in temperature, the FSD was delayed by about 1.6 d, the FED was advanced by about 3 d, and the FD was shortened by about 4.6 d; with each 1 degrees increase in latitude, the FSD was delayed by about 2.6 d, the FED was advanced by about 2.8 d, and the FD was shortened by about 5.6 d. The spatial variation in key variables of the soil freeze-thaw process under the same vegetation cover was closely related to latitude and altitude, where the lower the latitude and altitude, the more obvious the variation trend; among them, the interannual variation trend of key variables of soil freeze-thaw under meadow cover was the most obvious, which varied by 9.65, 16.86, and 26.51 d, respectively. In addition, the trends of ground temperature under different vegetation types were generally consistent, with the longest period of unstable freeze-thaw and the shortest period of stable freeze in coniferous forests, compared to the shortest period of unstable freeze-thaw and the longest period of stable freeze in meadows. The results of the study are important for our understanding of soil freeze-thaw processes and changes in Heilongjiang Province, as well as the evolution of high-latitude permafrost; they also promote further exploration of the impact of soil freeze-thaw on agricultural production and climate change.

In Arctic soils, warming accelerates decomposition of organic matter and increases emission of greenhouse gases (GHGs), contributing to a positive feedback to climate change. Although microorganisms play a key role in the processes between decomposition of organic matter and GHGs emission, the effects of warming on temporal responses of microbial activity are still elusive. In this study, treatments of warming and precipitation were conducted from 2012 to 2018 in Cambridge Bay, Canada. Soils of organic and mineral layers were collected monthly from June to September in 2018 and analyzed for extracellular enzyme activities and bacterial community structures. The activity of hydrolases was the highest in June and decreased thereafter over summer in both organic and mineral layers. Bacterial community structures changed gradually over summer, and the responses were distinct depending on soil layers and environmental factors; water content and soil temperature affected the shift of bacterial community structures in both layers, whereas bacterial abundance, dissolved organic carbon, and inorganic nitrogen did so in the organic layer only. The activity of hydrolases and bacterial community structures did not differ significantly among treatments but among months. Our results demonstrate that temporal variations may control extracellular enzyme activities and microbial community structure rather than the small effect of warming over a long period in high Arctic soil. Although the effects of the treatments on microbial activity were minor, our study provides insight that microbial activity may increase due to an increase in carbon availability, if the growing season is prolonged in the Arctic.

Land surface albedo (LSA) directly affects the radiation balance and the surface heat budget. LSA is a key variable for local and global climate research. The complexity of LSA variations and the driving factors highlight the importance of continuous spatial and temporal monitoring. Snow, vegetation and soil are the main underlying surface factors affecting LSA dynamics. In this study, we combined Global Land Surface Satellite (GLASS) products and ERA5 reanalysis products to analyze the spatiotemporal variation and drivers of annual mean blue-sky albedo for stable land cover types in the middle-high latitudes of the Northern Hemisphere (30~90 degrees N) from 1982 to 2015. Snow cover (SC) exhibited a decreasing trend in 99.59% of all pixels (23.73% significant), with a rate of -0.0813. Soil moisture (SM) exhibited a decreasing trend in 85.66% of all pixels (22.27% significant), with a rate of -0.0002. The leaf area index (LAI) exhibited a greening trend in 74.38% of all pixels (25.23% significant), with a rate of 0.0014. Blue-sky albedo exhibited a decreasing trend in 98.97% of all pixels (65.12% significant), with a rate of -0.0008 (OLS slope). Approximately 98.16% of all pixels (57.01% significant) exhibited a positive correlation between blue-sky albedo and SC. Approximately 47.78% and 67.38% of all pixels (17.13% and 25.3% significant, respectively) exhibited a negative correlation between blue-sky albedo and SM and LAI, respectively. Approximately 10.31%, 20.81% and 68.88% of the pixel blue-sky albedo reduction was mainly controlled by SC, SM and LAI, respectively. The decrease in blue-sky albedo north of 40 degrees N was mainly caused by the decrease in SC. The decrease in blue-sky albedo south of 40 degrees N was mainly caused by SM reduction and vegetation greening. The decrease in blue-sky albedo in the western Tibetan Plateau was caused by vegetation greening, SM increase and SC reduction. The results have important scientific significance for the study of surface processes and global climate change.

Land surface albedo plays a crucial role in the land surface energy budget and climate. This paper identified the spatiotemporal variations of surface albedo on the Tibetan Plateau (TP) from 1982 to 2015, and quantified the relationships between the spatial and temporal patterns of the albedo and associated influencing factors (snow cover, vegetation, and soil moisture) on the seasonal and interannual basis using satellite products and reanalysis data. It was determined that the albedo presented a distinct spatial variability, with high values in mountainous areas and low values on the southeastern TP. Spatially, average albedo exhibited a positive correlation with snow cover and negative correlations with vegetation and soil moisture. Average albedo over the whole TP had a clear seasonal cycle with a peak in winter and a minimum value in summer, which is dictated by seasonal changes in snow and vegetation covers. Annual average albedo exhibited a weakly downward trend, which was mainly contributed by a significant decrease in summer, pointing to the important role in vegetation dynamics for temporal change of the albedo. On the regional basis, interannual variation of albedo was more responsive to snow cover over the snow-and vegetation-coexisting area than the snow-covered area, and to changes in Normalized Difference Vegetation Index (NDVI) over the vegetation-covered area than the snow-and vegetation-coexisting area; albedo had a weakly negative correlation with soil moisture over bare soil. Furthermore, our results indicated that snow cover was the dominant factor for albedo change on mountainous areas, and vegetation change predominated the variation of albedo on the eastern, southern, and northwestern TP. Specifically, variations in snow cover contributed more than those of vegetation to the interannual albedo variation over the Three Rivers Headwater Region. These results would be beneficial for better understanding the climate and eco-environment changes over the TP. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).

Linking snow cover frequency (SCF) and atmospheric circulation is vital for comprehension of hemispheric-scale change mechanisms and for accurate forecasting. This study combined MODIS imagery with meteorological observations to investigate the variation of annual SCFs in the Qilian Mountains. Results indicated that more than 80% of annual SCF is distributed at high elevations and mostly on northern slopes, and that SCF is greater in the west than in the east. Abrupt change in the increase in annual SCF was not detected; however, significant (0.05 confidence level) variation with quasi-3-year and quasi-5-year periods indicated potential connection with monsoons. Topographically, SCF increased at high elevations and decreased in valleys. Moreover, SCF increased significantly with a rise in slope below 23 degrees and then decreased between 23 degrees and 45 degrees, and it decreased with a change in aspect from 70 degrees to 200 degrees and then increased from 200 degrees to 310 degrees. Annual SCF variation in the Qilian Mountains is dominated by precipitation rather than by temperature. In the years with high SCFs, southeasterly winds associated with an anticyclone over southeastern China and southwesterly winds associated with the cyclone over the Iranian Plateau brought warm moisture across northwestern China, favoring snowfall in the Qilian Mountains. Meanwhile, cold moisture outbreaks from the Arctic into the mid-latitudes are conducive to maintaining snow cover. However, in the years with low SCFs, the cold air might be difficultly transporting out of the Arctic region due to the strengthening polar vortex. Moreover, the water vapor was less than that of the mean state and divergence over the Qilian Mountains, which difficultly conduced snowfall over the Qilian Mountains.

Precipitation and snow/ice melt water are the primary water sources in inland river basins in arid areas, and these are sensitive to global climate change. A dataset of snow cover in the upstream region of the Shule River catchment was established using MOD10A2 data from 2000 to 2019, and the spatiotemporal variations in the snow cover and its meteorological, runoff, and topographic impacts were analyzed. The results show that the spatial distribution of the snow cover is highly uneven owing to altitude differences. The snow cover in spring and autumn is mainly concentrated along the edges of the region, whereas that in winter and summer is mainly distributed in the south. Notable differences in snow accumulation and melting are observed at different altitudes, and the annual variation in the snow cover extent shows bimodal characteristics. The correlation between the snow cover extent and runoff is most significant in April. The snow cover effectively replenishes the runoff at higher altitudes (3300-4900 m), but this contribution weakens with increasing altitude (>4900 m). The regions with a high snow cover frequency are mostly concentrated at high altitudes. Regions with slopes of 45 degrees. The snow cover frequency and slope aspect show symmetrical changes.

Wind erosion has notable impacts on ecology, water supply and regional climate, but its distributions and longterm changes are still poorly quantified for the Tibetan Plateau (TP). This study develops a coupled land-surface wind-erosion model (HRLDAS-WEPS) in two dimensions horizontally to analyze wind-erosion distributions and its temporal variations under the climate change in 1979?2015 over the entire TP. Two model enhancements are also used, including the application of MODIS vegetation datasets and the optimization of snow-cover parameterizations. Evaluation results indicate that the enhanced coupled model can generally represent the winderosion distributions over the TP, being mainly located in the arid and semi-arid areas and occurring in winter and spring, as compared with station observations and satellite datasets. In 1979?2015, wind erosion has a significant (P < 0.01) decreasing trend of -0.54 kg m- 2 yr- 1 for annual total soil loss averaged over the arid and semi-arid areas of the TP, which is mainly due to the significant (P < 0.01) declining wind speed and increasing soil moisture. The severest wind-erosion reduction is located to the northwest of the 200 mm precipitation line and the Qaidam Basin. Furthermore, a significant turning point of wind-erosion variation is found in 1992. Specifically, wind erosion over the TP decreases from 1979 to 1991 (-1.26 kg m- 2 yr- 1), and then stays at a low level with a slight increase (0.08 kg m- 2 yr- 1) since 1993. This is probably due to the abrupt change of wind speed over the TP in 1991.