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.

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.

The influence of seasonally frozen ground (SFG) on water, energy, and solute fluxes is important in cold climate regions. The hydrological role of permafrost is now being actively researched, but the influence of SFG has received less attention. Intuitively, SFG restricts (snowmelt) infiltration, thereby enhancing surface runoff and decreasing soil water replenishment and groundwater recharge. However, the reported hydrological effects of SFG remain contradictory and appear to be highly site- and event-specific. There is a clear knowledge gap concerning under what physiographical and climate conditions SFG is more likely to influence hydrological fluxes. We addressed this knowledge gap by systematically reviewing published work examining the role of SFG in hydrological partitioning. We collected data on environmental variables influencing the SFG regime across different climates, land covers, and measurement scales, along with the main conclusion about the SFG influence on the studied hydrological flux. The compiled dataset allowed us to draw conclusions that extended beyond individual site investigations. Our key findings were: (a) an obvious hydrological influence of SFG at small-scale, but a more variable hydrological response with increasing scale of measurement, and (b) indication that cold climate with deep snow and forest land cover may be related to reduced importance of SFG in hydrological partitioning. It is thus increasingly important to understand the hydrological repercussions of SFG in a warming climate, where permafrost is transitioning to seasonally frozen conditions.

Snowmelt water is a vital freshwater resource in the Altai Mountains of northwestern China. Yet its seasonal hydrological cycle characteristics could change under a warming climate and more rapid spring snowmelt. Here, we simulated snowmelt runoff dynamics in the Kayiertesi River catchment, from 2000 to 2016, by using an improved hydrological distribution model that relied on high-resolution meteorological data acquired from the National Centers for Environmental Prediction (Fnl-NCEP) that were downscaled using the Weather Research Forecasting model. Its predictions were compared to observed runoff data, which confirmed the simulations' reliability. Our results show the model performed well, in general, given its daily validation Nash-Sutcliffe efficiency (NSE) of 0.62 (from 2013 to 2015) and a monthly NSE score of 0.68 (from 2000 to 2010) for the studied river basin of the Altai Mountains. In this river basin catchment, snowfall accounted for 64.1% of its precipitation and snow evaporation for 49.8% of its total evaporation, while snowmelt runoff constituted 29.3% of the annual runoff volume. Snowmelt's contribution to runoff in the Altai Mountains can extend into non-snow days because of the snowmelt water retained in soils. From 2000 to 2016, the snow-to-rain ratio decreased rapidly, however, the snowmelt contribution remained relatively stable in the study region. Our findings provide a sound basis for making snowmelt runoff predictions, which could be used prevent snowmelt-induced flooding, as well as a generalizable approach applicable to other remote, high-elevation locations where high-density, long-term observational data are currently lacking. How snowmelt contributes to water dynamics and resources in cold regions is garnering greater attention. Our proposed model is thus timely perhaps, enabling more comprehensive assessments of snowmelt contributions to hydrological processes in those alpine regions characterized by seasonal snow cover.