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.

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.

The deposition of light absorbing impurities (LAIs) (e.g., black carbon (BC), organic carbon (OC), mineral dust (MD)) on snow is an important attribution to accelerate snowmelt across the northern Xinjiang, China. At present, there is still a lack of understanding of the LAIs concentration, elution and enrichment process in snow cover over Xinjiang. Based on these, continuously sampling during two years carried out to investigate the concentrations, impacts and potential sources of LAIs in snow at Kuwei Station in the southern Altai Mountains. The average concentrations of BC, OC and MD in the surface snow were 2787 +/- 2334 ng g(-1), 6130 +/- 6127 ng g(-1), and 70.03 +/- 62.59 mu g g(-1), respectively, which dramatically increased along with snowmelt intensified, reflecting a significant enrichment process of LAIs at the snow surface. Besides, high LAIs concentrations also found in the subsurface and melting layers of the snowpit, reflecting the elution and redistribution of LAIs. With the simulation of the SNow ICe Aerosol Radiative model, BC was the main dominant factor in reducing snow albedo and radiative forcing (RF), its impact was more remarkable in the snowmelt period. The average contribution rates of BC, MD and BC + MD to snow albedo reduction increased by 20.0 +/- 1.9%, 13.0 +/- 0.2%, and 20.5 +/- 2.3% in spring compared with that in winter; meanwhile, the corresponding average RFs increased by 15.8 +/- 3.4 W m(-2), 4.7 +/- 0.3 W m(-2) and 16.4 +/- 3.2 W m(-2), respectively. Changes in the number of snowmelt days caused by BC and MD decreased by 3.0 +/- 0.4 d to 8.3 +/- 1.3 d. It indicated that surface enrichment of LAIs during snow melting might accelerate snowmelt further. Weather Research and Forecasting Chemistry model showed that the resident emission was the main potential source of BC and OC in snow. This implied that the mitigation of intensive snowmelt needs to mainly reduce resident emission of LAIs in the future. (C) 2020 Elsevier Ltd. All rights reserved.

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.

In contrast to widespread glacier retreat evidenced globally, glaciers in the Karakoram region have exhibited positive mass balances and general glacier stability over the past decade. Snow and glacier meltwater from the Karakoram and the western Himalayas, which supplies the Indus River Basin, provide an essential source of water to more than 215 million people, either directly, as potable water, or indirectly, through hydroelectric generation and irrigation for crops. This study focuses on water resources in the Upper Indus Basin (UIB) which combines the ranges of the Hindukush, Karakoram and Himalaya (HKH). Specifically, we focus on the Gilgit River Basin (GRB) to inform more sustainable water use policy at the sub-basin scale. We employ two degree-day approaches, the Spatial Processes in Hydrology (SPHY) and Snowmelt Runoff Model (SRM), to simulate runoff in the GRB during 2001-2012. The performance of SRM was poor during July and August, the period when glacier melt contribution typically dominates runoff. Consequently, SPHY outperformed SRM, likely attributable to SPHY's ability to discriminate between glacier, snow, and rainfall contributions to runoff during the ablation period. The average simulated runoff revealed the prevalent snowmelt contribution as 62%, followed by the glacier melt 28% and rainfall 10% in GRB. We also assessed the potential impact of climate change on future water resources, based on two Representative Concentration Pathways (RCP) (RCP 4.5 and RCP 8.5). We estimate that summer flows are projected to increase by between 5.6% and 19.8% due to increased temperatures of between 0.7 and 2.6 degrees C over the period 2039-2070. If realized, increased summer flows in the region could prove beneficial for a range of sectors, but only over the short to medium term and if not associated with extreme events. Long-term projections indicate declining water resources in the region in terms of snow and glacier melt.

Hydrological conditions in cold regions have been shown to be sensitive to climate change. However, a detailed understanding of how regional climate and basin landscape conditions independently influence the current hydrology and its climate sensitivity is currently lacking. This study, therefore, compares the climate sensitivity of the hydrology of two basins with contrasted landscape and meteorological characteristics typical of eastern Canada: a forested boreal climate basin (Montmorency) versus an agricultural hemiboreal climate basin (Aca-die). The physically based Cold Regions Hydrological Modelling (CRHM) platform was used to simulate the current and future hydrological processes. Both basin landscape and regional climate drove differences in hy-drological sensitivities to climate change. Projected peak SWE were highly sensitive to warming, particularly for milder baseline climate conditions and moderately influenced by differences in landscape conditions. Landscape conditions mediated a wide range of differing hydrological processes and streamflow responses to climate change. The effective precipitation was more sensitive to warming in the forested basin than in the agricultural one, due to reductions in forest canopy interception losses with warming. Under present climate, precipitation and discharge were found to be more synchronized in the greater relief and slopes of the forested basin, whereas under climate change, they are more synchronized in the agricultural basin due to reduced infiltration and storage capacities. Flow through and over agricultural soils translated the increase in water availability under a warmer and wetter climate into higher peak discharges, whereas the porous forest soils dampened the response of peak discharge to increased available water. These findings help diagnose the mechanisms controlling hy-drological response to climate change in cold regions forested and agricultural basins.

Since the middle of the 20th century, the peak snowpack in the Upper Rio Grande (URG) basin of United States has been decreasing. Warming influences snowpack characteristics such as snow cover, snow depth, and Snow Water Equivalent (SWE), which can affect runoff quantity and timing in snowmelt runoff-dominated river systems of the URG basin. The purpose of this research is to investigate which variables are most important in predicting naturalized streamflow and to explore variables' relative importance for streamflow dynamics. We use long term remote sensing data for hydrologic analysis and deploy R algorithm for data processing and synthesizing. The data is analyzed on a monthly and baseflow/runoff basis for nineteen sub-watersheds in the URG. Variable importance and influence on naturalized streamflow is identified using linear standard regression with multi-model inference based on the second-order Akaike information criterion (AICc) coupled with the intercept only model. Five predictor variables: temperature, precipitation, soil moisture, sublimation, and SWE are identified in order of relative importance for streamflow prediction. The most influential variables for streamflow prediction vary temporally between baseflow and runoff conditions and spatially by watershed and mountain range. Despite the importance of temperature on streamflow, it is not consistently the most important factor in streamflow prediction across time and space. The dominance of precipitation over streamflow is more obvious during baseflow. The impact of precipitation, SWE, sublimation, and minimum temperature on streamflow is evident during the runoff season, but the results vary for different sub-watersheds. The association between sublimation and streamflow is positive in the runoff season, which may relate to temperature and requires further research. This research sheds light on the primary drivers and their spatial and temporal variability on streamflow generation. This work is critical for predicting how warming temperatures will impact water supplies serving society and ecosystems in a changing climate.

Historical patterns of snow cover and snowmelt are shifting due to climate warming and perhaps some human activities, threatening natural water resources and the ecological environment. Passive microwave remote sensing provides quantitative data for snow mass evaluation. Here, we evaluated the long-term impact of climate warming on snowmelt rates, using snow water equivalent (SWE) datasets derived from passive microwave remotely sensed data over China's three main stable snow cover regions during the past 40 years (1981-2020). The results showed that higher ablation rates in spring were found in locations with a deeper SWE because of high snowmelt rates that occurred in late spring and early summer in areas with a deeper snowpack. Annual maximum SWE (snow water equivalent) has declined across two out of the three main mountains of China's snow cover regions over the past 40 years under climate warming. The maximum and mean snowmelt rate was ca. 30 and 3 mm/day, respectively, over the three regions. Further, due to SWE being reduced in these deep snowpack areas, moderate and high rates of snowmelt showed trends of decline after 2000. Accordingly, an earlier snow onset day (average 0.6 similar to 0.7 day/a) and slower snowmelt rates characterized the mountainous areas across the three main snow cover regions. The slower snowmelt rate is also closely related to vegetation improvement over the three main stable snow cover regions. Therefore, not only vegetation in spring but also streamflow and other ecological processes could be affected by the pronounced changes in SWE and snowmelt rates. These findings strengthen our understanding of how to better assess ecological and environmental changes towards the sustainable use of freshwater resources in spring and earlier summer months in snow-rich alpine regions.