Snow plays a crucial role in the global water cycle, providing water to over 20% of the world’s population and serving as a vital component for flora, fauna, and climate regulation. Changes in snow patterns due to global warming have far-reaching impacts on water management, agriculture, and other economic sectors such as winter tourism. Additionally, they have implications for environmental stability, prompting migration and cultural shifts in snow-dependent communities. Accurate information on snow and its variables is, thus, essential for both scientific understanding and societal planning. This review explores the potential of remote sensing in monitoring snow water equivalent (SWE) on a large scale, analyzing 164 selected publications from 2000 to 2023. Categorized by methodology and content, the analysis reveals a growing interest in the topic, with a concentration of research in North America and China. Methodologically, there is a shift from passive microwave (PMW) inversion algorithms to artificial intelligence (AI), particularly the Random Forest (RF) and neural network (NN) approaches. A majority of studies integrate PMW data with auxiliary information, focusing thematically on remote sensing and snow research, with limited incorporation into broader environmental contexts. Long-term studies (>30 years) suggest a general decrease in SWE in the Northern Hemisphere, though regional and seasonal variations exist. Finally, the review suggests potential future SWE research directions such as addressing PMW data issues, downsampling for detailed analyses, conducting interdisciplinary studies, and incorporating forecasting to enable more widespread applications.

The snowpack in the European Alps is declining due to global warming, which affects both the amount of seasonal snow and the timing of accumulation and melt. As the European Alps is the largest winter tourism destination in the world by revenue, this decline in natural snow poses an existential threat to the sector. Several smaller ski areas have closed permanently since 1980, and all Alpine regions face rising costs due to an increasing reliance on snowmaking. Professional winter sports are also affected, with several canceled events in recent years due to unsuitable snow conditions. In this study, we present the first remote sensing-based assessment of long-term snow reliability for winter tourism in the European Alps. Using snowline elevation (SLE) data derived from Landsat observations from 1985 to 2024, combined with OpenStreetMap ski infrastructure data and digital elevation models, we quantified the monthly snow coverage of ski area segments across 43 Alpine basins. Theil–Sen trends and Mann–Kendall significances were calculated for the full season and for three subseasons, with quality checks applied to guarantee sufficient data coverage. The results show predominantly negative trends across all seasons, with the strongest declines occurring in the late season. In this period, 97.8% of all downhill ski areas and 99.5% of the cross-country ski areas for which a trend was derived exhibited negative trends. For the full season, the corresponding shares were 94% for downhill ski areas and 99.2% for cross-country ski areas. In addition, areas located at the geographical edges of the European Alps showed more pronounced negative trends compared with the core regions. These findings align with previous studies on the subject and highlight the ongoing shortening of natural snow seasons and thus the increased challenges for the winter tourism sector in the Alps.

Snowmelt is a vital contributor to river discharge across the Northern Hemisphere, supplying freshwater to over 1.5 billion people and supporting key economic sectors such as agriculture and hydropower. However, climate change has led to a decline in snow water equivalent (SWE) on almost the entire Northern Hemisphere, reducing snow-based water availability. Despite the importance of snowmelt and the pressure imposed by climate change, no large-scale studies have examined the connection of snowmelt dynamics and river discharge beyond statistical measures, which fail to capture the complexity of hydrological regimes. To address this gap, we perform causal discovery using PCMCI, a method that adapts the PC proposed by Peter Spirtes and Clark Glymour to the time series setting, to obtain qualitative causal structure across 119 basins from 1980 to 2022 and then quantify using causal effect estimation with a 20-year moving window and a random forest estimator. Our results show that the role of snowmelt in streamflow generation is changing. In various basins where the method allows for trend detection, the ratio of the causal effect of snowmelt on river discharge to the mean of the river discharge is increasing despite declining SWE. This suggests that as precipitation patterns shift and intra-annual variability increases, snowmelt may become more important for streamflow generation in certain basins despite a generally declining SWE. While regional differences emerge, causal effects do not consistently correlate with geographical factors such as latitude or basin characteristics. Analyses in six basins that serve as illustrative examples, indicate that changes in seasonal hydrology, particularly the timing and distribution of precipitation, influence the relative role snowmelt plays for river discharge. These findings highlight the power of causal inference over conventional statistical measures in enhancing the analysis of large-scale snow hydrological regimes by adding depth to existing approaches.

Snowmelt is a vital contributor to river discharge across the Northern Hemisphere, supplying freshwater to over 1.5 billion people and supporting key economic sectors such as agriculture and hydropower. However, climate change has led to a decline in snow water equivalent (SWE) on almost the entire Northern Hemisphere, reducing snow-based water availability. Despite the importance of snowmelt and the pressure imposed by climate change, no large-scale studies have examined the connection of snowmelt dynamics and river discharge beyond statistical measures, which fail to capture the complexity of hydrological regimes. To address this gap, we perform causal discovery using PCMCI, a method that adapts the PC proposed by Peter Spirtes and Clark Glymour to the time series setting, to obtain qualitative causal structure across 119 basins from 1980 to 2022 and then quantify using causal effect estimation with a 20-year moving window and a random forest estimator. Our results show that the role of snowmelt in streamflow generation is changing. In various basins where the method allows for trend detection, the ratio of the causal effect of snowmelt on river discharge to the mean of the river discharge is increasing despite declining SWE. This suggests that as precipitation patterns shift and intra-annual variability increases, snowmelt may become more important for streamflow generation in certain basins despite a generally declining SWE. While regional differences emerge, causal effects do not consistently correlate with geographical factors such as latitude or basin characteristics. Analyses in six basins that serve as illustrative examples, indicate that changes in seasonal hydrology, particularly the timing and distribution of precipitation, influence the relative role snowmelt plays for river discharge. These findings highlight the power of causal inference over conventional statistical measures in enhancing the analysis of large-scale snow hydrological regimes by adding depth to existing approaches.

Snowmelt is a vital contributor to river discharge across the Northern Hemisphere, supplying freshwater to over 1.5 billion people and supporting key economic sectors such as agriculture and hydropower. However, climate change has led to a decline in snow water equivalent (SWE) on almost the entire Northern Hemisphere, reducing snow-based water availability. Despite the importance of snowmelt and the pressure imposed by climate change, no large-scale studies have examined the connection of snowmelt dynamics and river discharge beyond statistical measures, which fail to capture the complexity of hydrological regimes. To address this gap, we perform causal discovery using PCMCI, a method that adapts the PC proposed by Peter Spirtes and Clark Glymour to the time series setting, to obtain qualitative causal structure across 119 basins from 1980 to 2022 and then quantify using causal effect estimation with a 20-year moving window and a random forest estimator. Our results show that the role of snowmelt in streamflow generation is changing. In various basins where the method allows for trend detection, the ratio of the causal effect of snowmelt on river discharge to the mean of the river discharge is increasing despite declining SWE. This suggests that as precipitation patterns shift and intra-annual variability increases, snowmelt may become more important for streamflow generation in certain basins despite a generally declining SWE. While regional differences emerge, causal effects do not consistently correlate with geographical factors such as latitude or basin characteristics. Analyses in six basins that serve as illustrative examples, indicate that changes in seasonal hydrology, particularly the timing and distribution of precipitation, influence the relative role snowmelt plays for river discharge. These findings highlight the power of causal inference over conventional statistical measures in enhancing the analysis of large-scale snow hydrological regimes by adding depth to existing approaches.