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.

In this study, air pollutants were analyzed at a low-industry city on the Silk Road Economic Belt of Northwestern China from 2015 to 2018. The results show that SO2 and CO had a decreasing trend and NO2, O-3, PM2.5, and PM10 had an increasing trend during the study period. The primary characteristic pollutants were PM2.5 and PM10, which were higher than China's Grade II standard. SO2, NO2, CO, PM2.5, and PM10 concentrations showed similar seasonal variation patterns: the highest pollutant concentration was in winter and the lowest in summer. Those pollutants showed a similar diurnal pattern with two peaks, one at 7:00 to 9:00 and another at 21:00 to 22:00. However, O-3 concentration was highest in summer and lowest in winter, with a unimodal diurnal variation pattern. The annual average pollution concentrations in Tianshui in 2017 were substantially lower than the concentrations reported by most cities in China. By examining the meteorological conditions at a daily scale, we found that Tianshui was highly influenced by local emissions and a southwest wind. Potential source contributions and concentration weighted trajectory analyses indicated that the pollution from Gansu, Sichuan, Qinghai, and Shaanxi Province could affect the pollution concentration in Tianshui. The results provide directions for the government to take in formulating regional air pollution prevention and control measures and to improve air quality.

The of the Yellow River between its source and Hekou Town in Inner Mongolia is known as the Upper Yellow River Basin. It is the main source area of water resources in the Yellow River Basin, providing reliable water resources for 120 million people. Studying the hydrometeorological changes in the Upper Yellow River Basin is crucial for the development of human society. However, in the past, there has been limited research on hydrometeorological changes in the Upper Yellow River Basin. In order to clarify the four-dimensional spatiotemporal variation characteristics of hydrometeorological elements in the Upper Yellow River Basin, satellite and reanalysis hydrometeorological elements products need to be used. Unfortunately, there is currently a lack of precise evaluation studies on satellite and reanalysis hydrometeorological elements products in the Upper Yellow River Basin, and the geomorphic characteristics of this area have raised doubts about the accuracy of satellite and reanalysis hydrometeorological elements products. Thus, the evaluation study in the Upper Yellow River Basin is an important prerequisite for studying the four-dimensional spatiotemporal changes of hydrometeorological elements. When conducting evaluation study, we found that previous evaluation studies had a very confusing understanding of the spatiotemporal characteristics of datasets. Some papers even treated the spatiotemporal characteristics of evaluation metrics as the spatiotemporal characteristics of datasets. Therefore, we introduced a four-dimensional spacetime of both datasets and evaluation metrics to rectify the chaotic spatiotemporal view in the past. Our research results show that satellite and reanalysis hydrometeorological elements products have different abilities in describing the temporal and spatial distribution and change characteristics of hydrometeorological elements. The difference in the ability of satellite and reanalysis hydrometeorological elements products to describe temporal and spatial distribution and change characteristics requires us to select data at different temporal and spatial scales according to research needs when conducting hydrometeorological research, in order to ensure the credibility of the research results.

Downward solar radiation (DSR) and air temperature (Ta) have significant influences on the thermal state of frozen ground. These parameters are also important forcing terms for physically based land surface models (LSMs). However, the quantitative influences of inaccuracies in DSR and Ta products on simulated frozen ground temperatures remain unclear. In this study, three DSR products (CMFD-SR, Tang-SR, and GLDAS-SR) and two Ta products (CMFD-Ta and GLDAS-Ta) were used to force an LSM model in an alpine watershed in Northwest China, to investigate the sensitivity of simulated ground temperatures to different DSR and Ta products. Compared to a control model (CTRL) forced by in situ observed DSR, ground temperatures simulated by the experimental model forced by GLDAS-SR are obviously decreased because GLDAS-SR is much lower than in situ observations. Instead, simulation results in models forced by CMFD-SR and Tang-SR are much closer to those of CTRL. Ta products led to significant errors in simulated ground temperatures. In conclusion, both CMFD-SR and Tang-SR could be used as good alternatives to in situ observed DSR for forcing a model, with acceptable errors in simulation results. However, more care need to be paid for models forced by Ta products instead of Ta observations, and conclusions should be carefully drawn.

Drought is a major natural disaster worldwide. Understanding the correlation between meteorological drought (MD) and agricultural drought (AD) is essential for relevant policymaking. In this paper, standardized precipi-tation evapotranspiration index and standardized soil moisture index were used to estimate the MD and AD in the North China Plain (NCP) to identify the correlation between MD and AD during the growth period of winter wheat. In addition, we investigated the contributions of climate change (CC) and human activity (HA) to AD and the factors influencing the loss of winter wheat net primary production (NPP). Drought propagation time (PT) increased spatially from the southern to northern NCP (from 3 to 11 months). PT first increased and then decreased during the phenological period of winter wheat, and the decreasing trend was delayed with an increasing latitude. In general, the relative contribution of CC to AD was higher than that of HA; the correlation between MD and AD exhibited a weakening trend, particularly during the middle and late phenological stages of winter wheat. Precipitation was the main driver of the effects of HA on AD; the effects were stronger in areas with less precipitation. However, because of the improved irrigation conditions and scarce rainfall during the growth period of winter wheat in the study area, the effects of precipitation on AD were nonsignificant. Instead, tem-perature, wind, and total solar radiation, which are highly correlated with evapotranspiration, were identified as the primary drivers of AD; spatiotemporal variations were noted in these correlations. Prolonged drought PT reduced NPP; the sensitivity of winter wheat NPP to AD was higher in humid areas than in semiarid or semi-humid areas. NPP loss occurred primarily due to HA. Our findings revealed a correlation between MD and AD in agroecosystems and may facilitate policymaking related to drought mitigation and food security.

Snow, as a fundamental reservoir of freshwater, is a crucial natural resource. Specifically, knowledge of snow density spatial and temporal variability could improve modelling of snow water equivalent, which is relevant for managing freshwater resources in context of ongoing climate change. The possibility of estimating snow density from remote sensing has great potential, considering the availability of satellite data and their ability to generate efficient monitoring systems from space. In this study, we present an innovative method that combines meteorological parameters, satellite data and field snow measurements to estimate thermal inertia of snow and snow density at a catchment scale. Thermal inertia represents the responsiveness of a material to variations in temperature and depends on the thermal conductivity, density and specific heat of the medium. By exploiting Landsat 8 data and meteorological modelling, we generated multitemporal thermal inertia maps in mountainous catchments in the Western European Alps (Aosta Valley, Italy), from incoming shortwave radiation, surface temperature and snow albedo. Thermal inertia was then used to develop an empirical regression model to infer snow density, demonstrating the possibility of mapping snow density from optical and thermal observations from space. The model allows for estimation of snow density with R-CV(2) and RMSECV of 0.59 and 82 kg m(-3), respectively. Thermal inertia and snow density maps are presented in terms of the evolution of snow cover throughout the hydrological season and in terms of their spatial variability in complex topography. This study could be considered a first attempt at using thermal inertia toward improved monitoring of the cryosphere. Limitations of and improvements to the proposed methods are also discussed. This study may also help in defining the scientific requirements for new satellite missions targeting the cryosphere. We believe that a new class of Earth Observation missions with the ability to observe the Earth's surface at high spatial and temporal resolution, with both day and night-time overpasses in both optical and thermal domain, would be beneficial for the monitoring of seasonal snowpacks around the globe.

Arctic zone of the Russian Federation (AZRF) is the region of intensive economic development. In this regard, it is critical to give an adequate assessment of natural factors that may have a negative impact on the growing technological infrastructure. Rapid climate change effects show a significant influence on this activity, including the railway network development. Hence, the decision-making community requires relevant information on climatic variations that can put at hazard the construction and operation of railway facilities. This paper presents the analysis of climatic changes within the region of Central and Western Russian Arctic in 1980-2021. It was performed using the new electronic Atlas of climatic variations in main hydrometeorological parameters, created for the Russian Railways in 2023. This geoinformatic product includes about 400 digital maps reflecting the variability of seven climatic parameters over more than four decades within the studied region. These parameters are air temperature, total precipitation, wind speed, soil temperature, soil moisture content, air humidity, and snow cover thickness. The analysis of climatic maps and their comparison between selected periods showed spatial and temporal heterogeneity of climatic variations in this region. This justifies the feasibility of further research using additional analytical instruments, such as Hovm & ouml;ller diagrams, time series graphs, etc. The implementation of advanced geoinformatic products in the practice of the Russian Railways will facilitate sustainable development of its infrastructure in rapidly altering climatic conditions.

The effects of the present global climate change appear more pronounced in high latitudes and alpine regions. Transitions zones, such as the southern fringe of the boreal region in northern Mongolia, are expected to experience drastic changes as a result. This area is dry and cold with forests forming only on the north-facing slopes of hills and grasslands distributing on the south-facing slopes, making it difficult for continuous forests to exist. However, in the Hovsgol Lake Basin, there is a vast continuous pure forest of Siberian larch (Larix sibirica). In other words, the lake water thawing/freezing process may have created a unique climatic environment that differs with the climate of the adjacent Darhad Basin, where no lake exists. Thus, in order to compare the effect of the thawing/freezing dynamics of lake water and the active layer on the thermal regime at each basin, respectively, temperatures were simultaneously measured. The Darhad Basin has similar latitude, topography, area, and elevation conditions. As expected, the presence of the lake affected the annual temperature amplitude, as it was 60% of that in the Darhad Basin. The difference in the seasonal freeze-thaw cycles of the lake and the active layer caused a significant difference in the thermal regime, especially in winter.

Soil moisture dynamics play an active role in ecological and hydrological processes. Although the variation of the soil water moisture of multiple ecosystems have been well-documented, few studies have focused on soil hydrological properties by using a drying and weighing method in a long time series basis in the Qinghai-Tibet Plateau (QTP). In this study, 13 year (2008-2020) time-series observational soil moisture data and environmental factors were analyzed in a humid alpine Kobresia meadow on the Northern Qinghai-Tibetan Plateau. The results showed no significant upward trend in soil water content during the 2008-2020 period. In the growth season (May-October), the soil water content showed a trend of decreasing firstly, then increasing, and finally, decreasing. Correlation analysis revealed that five meteorology factors (temperature, humidity, net radiation, dew point temperature, and vapor pressure) and a biomass element (above-ground biomass) had a significant effect on the soil moisture, and air temperature impacted the soil water variation negatively in 0-50 cm, indicating that global warming would reduce soil moisture. Humidity and net radiation made a difference on shallow soil (0-10 cm), while dew point temperature and vapor pressure played a role on the deep soil (30-50 cm). Above-ground biomass only effected 30-50 cm soil moisture variation, and underground biomass had little effect on the soil moisture variation. This indirectly indicated that below-ground biomass is not limited by soil moisture. These results provide new insights for the rational allocation of water resources and management of vegetation in alpine meadows, in the context of climate change.

Equivalent black carbon (EBC) was measured with a seven-wavelength Aethalometer (AE-31) in the Urumqi River Valley, eastern Tien Shan, China. This is the first high-resolution, online measurement of EBC conducted in the eastern Tien Shan allowing analysis of the seasonal and hourly variations of the light absorption properties of EBC. Results showed that the highest concentrations of EBC were in autumn, followed by those in summer. The hourly variations of EBC showed two plateaus during 8:00-9:00 h local time (LT) and 16:00-19:00 h LT, respectively. The contribution of biomass burning to EBC in winter and spring was higher than in summer and autumn. The planetary boundary layer height (PBLH) showed an inverse relationship with EBC concentrations, suggesting that the reduction of the PBLH leads to enhanced EBC. The aerosol optical depths (AOD) over the Urumqi River Valley, derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data and back trajectory analysis, showed that the pollution from Central Asia was more likely to affect the atmosphere of Tien Shan in summer and autumn. This suggests that long-distance transported pollutants from Central Asia could also be potential contributors to EBC concentrations in the Urumqi River Valley, the same as local anthropogenic activities.