Seasonal snowpack represents the dominant source of water for streamflow, reservoir storage, agricultural irrigation, groundwater recharge, and hydroelectric power production in several regions around the world. Historical observations have revealed a steady trend of declining snowpack water storage and snow cover extent, as well as earlier melt onset and snow disappearance driven by the changing climate on a broad scale. As a result, the majority of high-mountain Asia already experiences streamflow reduction during March and May, when the monsoon has not fully commenced. Many other mountainous regions are also witnessing changes in snowpack and streamflow patterns with downstream impacts on ecosystems and water availability.
Snowmelt, and in particular Rain-on-Snow (ROS), is a key driver of the most extreme flooding events, particularly in the northern maritime regions impacted by atmospheric rivers. Furthermore, variations in forest structures resulting from natural and/or anthropogenic impacts (e.g., climate, forest management, wildfire, etc.) influence the spatiotemporal variability of the snowpack and its changes, which makes snow-related hydrological events difficult to predict. Recent research using high-resolution LiDAR-derived forest and snow data has highlighted the influence of canopy structure on snow processes, and the importance of improving fine-scale representation of forest-snow processes to advance process-scale understanding of forest-snow interactions and modeling of snow hydrological processes at the scales at which management decisions are made.
In this Research Topic we invite manuscripts that aim to advance the understanding of snow processes, forest-snow interactions, and snow hydrology across scales through observations, model simulations, or coupled approaches. Topics of interest include, but are not limited to:
(1) Analyses of field measurements or remote-sensing data to better understand snow processes and the factors influencing snowpack spatiotemporal variability across scales, such as snowpack energy fluxes, snow-canopy processes, and forest canopy structure;
(2) Model development to improve parameterization of snow hydrological processes such as improved representation of canopy characterization and canopy impact on snow energy balance;
(3) Tracing of snowmelt in the hydrological process, accounting for uncertainties associated with the methods used at various scales;
(4) Intercomparison of models to provide a better understanding of how well current modeling capabilities capture key snow hydrological processes and snow-related hydrological extremes such as rain-on-snow events and snow droughts;
(5) Identification of observational needs for improving snow hydrological predictions; and,
(6) Climate and anthropogenic impacts on snow processes and the implications for hydrological processes, hydrological extremes, and climatological trends at different spatial scales.
Seasonal snowpack represents the dominant source of water for streamflow, reservoir storage, agricultural irrigation, groundwater recharge, and hydroelectric power production in several regions around the world. Historical observations have revealed a steady trend of declining snowpack water storage and snow cover extent, as well as earlier melt onset and snow disappearance driven by the changing climate on a broad scale. As a result, the majority of high-mountain Asia already experiences streamflow reduction during March and May, when the monsoon has not fully commenced. Many other mountainous regions are also witnessing changes in snowpack and streamflow patterns with downstream impacts on ecosystems and water availability.
Snowmelt, and in particular Rain-on-Snow (ROS), is a key driver of the most extreme flooding events, particularly in the northern maritime regions impacted by atmospheric rivers. Furthermore, variations in forest structures resulting from natural and/or anthropogenic impacts (e.g., climate, forest management, wildfire, etc.) influence the spatiotemporal variability of the snowpack and its changes, which makes snow-related hydrological events difficult to predict. Recent research using high-resolution LiDAR-derived forest and snow data has highlighted the influence of canopy structure on snow processes, and the importance of improving fine-scale representation of forest-snow processes to advance process-scale understanding of forest-snow interactions and modeling of snow hydrological processes at the scales at which management decisions are made.
In this Research Topic we invite manuscripts that aim to advance the understanding of snow processes, forest-snow interactions, and snow hydrology across scales through observations, model simulations, or coupled approaches. Topics of interest include, but are not limited to:
(1) Analyses of field measurements or remote-sensing data to better understand snow processes and the factors influencing snowpack spatiotemporal variability across scales, such as snowpack energy fluxes, snow-canopy processes, and forest canopy structure;
(2) Model development to improve parameterization of snow hydrological processes such as improved representation of canopy characterization and canopy impact on snow energy balance;
(3) Tracing of snowmelt in the hydrological process, accounting for uncertainties associated with the methods used at various scales;
(4) Intercomparison of models to provide a better understanding of how well current modeling capabilities capture key snow hydrological processes and snow-related hydrological extremes such as rain-on-snow events and snow droughts;
(5) Identification of observational needs for improving snow hydrological predictions; and,
(6) Climate and anthropogenic impacts on snow processes and the implications for hydrological processes, hydrological extremes, and climatological trends at different spatial scales.
