Light-absorbing impurities (LAIs), such as mineral dust (MD), organic carbon (OC), and black carbon (BC), deposited in snow, can reduce snow albedo and accelerate snowmelt. The Ili Basin, influenced by its unique geography and westerly atmospheric circulation, is a critical region for LAI deposition. However, quantitative assessments on the impact of LAIs on snow in this region remain limited. This study investigated the spatial distribution of LAIs in snow and provided a quantitative evaluation of the effects of MD and BC on snow albedo, radiative forcing, and snowmelt duration through sampling analysis and model simulations. The results revealed that the Kunes River Basin in the eastern Ili Basin exhibited relatively high concentrations of MD. In contrast, the southwestern Tekes River Basin showed relatively high concentrations of OC and BC. Among the impurities, MD plays a dominant role in the reduction of snow albedo and has a greater effect on the absorption of solar radiation by snow than BC, while MD is the most important light-absorbing impurity responsible for the reduction in the number of snow-melting days in the Ili Basin. Under the combined influence of MD and BC, the snowmelt period in the Ili Basin was reduced by 2.19 +/- 1.43 to 7.31 +/- 4.76 days. This study provides an initial understanding of the characteristics of LAIs in snow and their effects on snowmelt within the Ili Basin, offering essential basic data for future research on the influence of LAIs on snowmelt runoff and hydrological processes in this region.

In alpine tundra regions, snowmelt plays a crucial role in creating spatial heterogeneity in soil moisture and nutrients across various terrains, influencing vegetation distribution. With climate warming, snowmelt has advanced, lengthening the growing season while also increasing the risk of frost damage to evergreen dwarf shrubs like Rhododendron aureum in alpine tundra regions. To understand these long-term effects, we used remote sensing imagery to analyze nearly four decades (1985-2022) of snowmelt date and the distribution change of R. aureum in Changbai Mountain, East China's only alpine tundra. Results show that snowmelt advanced by 1-3 days/10 years, with faster rates at higher elevations and shady slopes (0.4-0.6 days/10 years more than sunny slopes), while R. aureum increased more on shady slopes under such conditions. Our study demonstrates that these shifts in snowmelt date vary significantly across topographies and reveals how topography and snowmelt changes interact to shape the distribution of evergreen shrubs under climate warming.

In the mountainous headwaters of the Colorado River episodic dust deposition from adjacent arid and disturbed landscapes darkens snow and accelerates snowmelt, impacting basin hydrology. Patterns and impacts across the heterogenous landscape cannot be inferred from current in situ observations. To fill this gap daily remotely sensed retrievals of radiative forcing and contribution to melt were analyzed over the MODIS period of record (2001-2023) to quantify spatiotemporal impacts of snow darkening. Each season radiative forcing magnitudes were lowest in early spring and intensified as snowmelt progressed, with interannual variability in timing and magnitude of peak impact. Over the full record, radiative forcing was elevated in the first decade relative to the last decade. Snowmelt was accelerated in all years and impacts were most intense in the central to southern headwaters. The spatiotemporal patterns motivate further study to understand controls on variability and related perturbations to snow water resources.

In this study, we used satellite observations to identify 10 typical dust-loading events over the Indian Himalayas. Next, the aerosol microphysical and optical properties during these identified dust storms are characterized using cotemporal in situ measurements over Mukteshwar, a representative site in Indian Himalayas. Relative to the background values, the mass of coarse particles (size range between 2.5 and 10 mu m) and the extinction coefficient were found to be enhanced by 400% (from 24 +/- 15 to 98 +/- 40 mu g/m3) and 175% (from 89 +/- 57 Mm-1 to 156 +/- 79 Mm-1), respectively, during these premonsoonal dust-loading events. Moreover, based on the air mass trajectory, these dust storms can be categorized into two categories: (a) mineral dust events (MDEs), which involve long-range transported dust plumes traversing through the lower troposphere to reach the Himalayas and (b) polluted dust events (PDEs), which involve short-range transported dust plumes originating from the arid western regions of the Indian subcontinent and traveling within the heavily polluted boundary layer of the Gangetic plains before reaching the Himalayas. Interestingly, compared to the background, the SSA and AAE decrease during PDEs but increase during MDEs. More importantly, we observe a twofold increase in black carbon concentrations and the aerosol absorption coefficient (relative to the background values) during the PDEs with negligible changes during MDEs. Consequently, the aerosol-induced snow albedo reduction (SAR) also doubles during MDEs and PDEs relative to background conditions. Thus, our findings provide robust observational evidence of substantial dust-induced snow and glacier melting over the Himalayas.

Atmospheric particulate matter (PM) as light-absorbing particles (LAPs) deposited to snow cover can result in early onset and rapid snow melting, challenging management of downstream water resources. We identified LAPs in 38 snow samples (water years 2013-2016) from the mountainous Upper Colorado River basin by comparing among laboratory-measured spectral reflectance, chemical, physical, and magnetic properties. Dust sample reflectance, averaged over the wavelength range of 0.35-2.50 mu m, varied by a factor of 1.9 (range, 0.2300-0.4444) and was suppressed mainly by three components: (a) carbonaceous matter measured as total organic carbon (1.6-22.5 wt. %) including inferred black carbon, natural organic matter, and carbon-based synthetic, black road-tire-wear particles, (b) dark rock and mineral particles, indicated by amounts of magnetite (0.11-0.37 wt. %) as their proxy, and (c) ferric oxide minerals identified by reflectance spectroscopy and magnetic properties. Fundamental compositional differences were associated with different iron oxide groups defined by dominant hematite, goethite, or magnetite. These differences in iron oxide mineralogy are attributed to temporally varying source-area contributions implying strong interannual changes in regional source behavior, dust-storm frequency, and (or) transport tracks. Observations of dust-storm activity in the western U.S. and particle-size averages for all samples (median, 25 mu m) indicated that regional dust from deserts dominated mineral-dust masses. Fugitive contaminants, nevertheless, contributed important amounts of LAPs from many types of anthropogenic sources.

Due to the impact of climate change, significant alterations in snowmelt have already occurred, which have been demonstrated to play a crucial role in photosynthetic carbon sequestration processes in vegetation. However, the effect of changes in snowmelt on light use efficiency (LUE) of grassland remain largely unknown in the permafrost region of Qinghai-Tibetan Plateau (QTP). By utilizing remote sensing data from 2000 to 2017, we conducted an analysis on the spatial and temporal patterns of LUE for various types of permafrost and grassland on the QTP. The LUE of the growing season was 1.1588 g CMJ(-1), displaying variations among different ecosystems: alpine steppe of seasonally frozen ground (ASS) > alpine meadow of seasonally frozen ground (AMS) > alpine meadow of permafrost (AMP) > alpine steppe of permafrost (ASP). Furthermore, our study demonstrated that decreasing snowmelt during the growing season had a negative impact on LUE through meteorological factors, elucidating its influence on LUE for approximately 40.65%, 34.06%, 41.05%, and 32.68% of ecosystems studied. Reduced snowmelt indirectly affects LUE by lowering air temperatures, vapor pressure deficit and solar radiation, while replenishing soil moisture. Additionally, changes in snowmelt can directly affect LUE by reducing the insulating properties of snow cover. Therefore, when estimating gross primary productivity (GPP) using remote sensing data based on LUE, it is essential to consider the impact of snowmelt. This will better represent vegetation phenology's response to climate change.

For rigorous understanding the shallow landslide mechanisms and deformation characteristics of expansive soil slopes, a comprehensive in-situ monitoring platform is established. Triaxial creep tests and microstructure analysis with scanning electron microscopy are also conducted on expansive soil samples obtained from Binxi station. Field monitoring data indicates that freeze-thaw (F-T) cycle and snowmelt infiltration significantly increase the creep deformation of expansive soil slope during spring melting period. Due to the influence of F-T cycle and snowmelt infiltration, more soil grains are involved in the shear deformation contributing to a large, localized shearing. Additionally, the microstructural analysis shows that F-T cycle influences the relationship between expansive soil grains that gradually change from face-face contact to point-face contact or edge-edge contact form. The shallow landslide mechanisms of expansive soil slope are revealed from creep deformation and microstructure characteristics of soils after the F-T cycle and snowmelt infiltration, which can be summarized into two stages, namely, the snowfall accumulation state and snow melt-shallow infiltration stage. These results can serve as a good reference for the prevention of expansive soil slopes in seasonally frozen regions.

In the context of global research in snow-affected regions, research in the Australian Alps has been steadily catching up to the more established research environments in other countries. One area that holds immense potential for growth is hydrological modelling. Future hydrological modelling could be used to support a range of management and planning issues, such as to better characterise the contribution of the Australian Alps to flows in the agriculturally important Murray-Darling Basin despite its seemingly small footprint. The lack of recent hydrological modelling work in the Australian Alps has catalysed this review, with the aim to summarise the current state and to provide future directions for hydrological modelling, based on advances in knowledge of the Australian Alps from adjacent disciplines and global developments in the field of hydrologic modelling. Future directions proffered here include moving beyond the previously applied conceptual models to more physically based models, supported by an increase in data collection in the region, and modelling efforts that consider non-stationarity of hydrological response, especially that resulting from climate change.

In 2022, the Pakistan witnessed the hottest spring and wettest summer in history. And devastating floods inundated a large portion of Pakistan and caused enormous damages. However, the primary water source and its contributions to these unprecedented floods remain unclear. Based on the reservoir inflow measurements, Multi-Source Weighted-Ensemble Precipitation (MSWEP), the fifth generation ECMWF atmospheric reanalysis (ERA5) products, this study quantified the contributions of monsoon precipitation, antecedent snowmelts, and orographic precipitation enhancement to floods in Pakistan. We found that the Indus experienced at least four inflow uprushes, which was mainly supplied by precipitation and snowmelt; In upper Indus, abnormally high temperature continued to influence the whole summer and lead to large amounts of snowmelts which not only was a key water supply to the flood but also provided favorable soil moisture conditions for the latter precipitation. Before July, the snowmelt has higher contributions than the precipitation to the streamflow of Indus River, with contribution value of more than 60%. Moreover, the snowmelt could still supply 20%-40% water to the lower Indus in July and August; The leading driver of 2022 mega-floods over the southern Pakistan in July and August was dominated by the precipitation, where terrain disturbance induced precipitation account to approximately 33% over the southern Pakistan. The results help to understand the mechanisms of flood formation, and to better predict future flood risks over complex terrain regions.

As a vital freshwater resource for one-sixth of the world's population, snowmelt provides great convenience for residents in terms of livelihood and production, agricultural irrigation, and hydroelectric power generation. However, snowmelt can also have an important impact on the formation of surface runoff and the process of soil erosion. In contrast to glacier melt, snowmelt erosion has received relatively little attention in the past. This paper reviewed the generation of snowmelt runoff, the characteristics of erosion and sediment yield during snowmelt, the snowmelt erosion mechanism, and the applications of snowmelt modeling. The published results of sediment yield driven from snowmelt runoff ranged from 1 to 300 t km-2 a-1, with the largest value of 1114 t km-2 a-1. Snowmelt erosion is extremely sensitive to warming climate. With global warming, there is a trend towards earlier snowmelt periods and a significant increase in runoff volume, as well as a significant increase in sediment yield from snowmelt in most of the study cases. Moreover, snowmelt erosion compared to rainfall erosion has more complex mechanistic processes which can be influenced by various factors such as snowfall, freeze-thaw, topography, etc. In particular, the occurrence of rain-on-snow events will lead to more severe soil erosion. In addition, current studies of sediment yield from snowmelt erosion account for a small percentage of snowmelt, and snowmelt erosion modeling is rarely applied in practical studies. In future research, the field monitoring of snowmelt erosion in the context of climate change needs to be further strengthened and the effects of multiple factors on snowmelt erosion need to be investigated. The inclusion of rain-on-snow and specific erosion types in the model will improve the applicability of models under climate change scenarios and in multiscale environments. This paper is intended to show the achievements as well as the limitations of snowmelt erosion research, while suggesting future research directions that need to be further explored and developed for better understanding and forecasting of snowmelt erosion.