Improving snowpack chemistry simulations through improved representation of liquid water movement through layered snow and rain-on-snow (ROS) episodes: Application to Svalbard, Norway

dc.contributor.authorDiogo, Costa
dc.contributor.authorAndrea, Spolaor
dc.contributor.authorElena, Barbaro
dc.contributor.authorJuan I., López-Moreno
dc.contributor.authorJohn W., Pomeroy
dc.date.accessioned2025-02-18T15:49:04Z
dc.date.available2025-02-18T15:49:04Z
dc.date.issued2025
dc.description.abstractCircumpolar and high-elevation cold regions receive a large portion of their annual precipitation as snowfall, which accumulates in snowpacks that can store many contaminants. The discharge of chemical eluent during snowmelt can alter the chemical composition of local streams and have a detrimental effect on aquatic ecosystems. Cold regions have been particularly affected by climate change. In the last two decades, the Arctic has been exposed to dramatic atmospheric temperature increases, sea ice decrease, and an increase of air mass transport from lower latitudes bringing warmer and more humid air masses. Instrumental measurements in the Svalbard archipelago, Norway, show that climate warming here is amplified compared to the global average, making its cryospheric environment extremely vulnerable to future climate scenarios. In this study, the PULSE model for simulation of snowpack solute dynamics was coupled to two snowpack energy balance models, the Cold Regions Hydrological Model and the SNOWPACK model, to help identify critical processes needed to improve the accuracy of snow chemistry predictions. Focus was given to to represent sea spray sources, to represent terrestrial dust, and to represent various sources including sea salt, biogenic emissions, and long-range atmospheric transport of secondary aerosols. The new coupled models were applied to an experimental site in Svalbard. The hydrological components of each model coupling were validated against snowdepth measurements and the snowpack chemistry components were verified for a selected number of snow ions representative of different sources. Both models were able to predict snowdepths between 1996 and 2018, as well as the stratification of snow chemistry measured during a whole snow accumulation and ablation year. Results show that explicitly representing liquid water movement through layered snow helped improve chemistry predictions. Events such as rain-on-snow (ROS) had a disproportionate effect on the redistribution of ions to deeper snow layers.por
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dc.identifier.citationCosta, D., Spolaor, A., Barbaro, E., López-Moreno, J. I., & Pomeroy, J. W. (2025). Improving snowpack chemistry simulations through improved representation of liquid water movement through layered snow and rain-on-snow (ROS) episodes: Application to Svalbard, Norway. Journal of Hydrology, 651, 132573. https://doi.org/https://doi.org/10.1016/j.jhydrol.2024.132573por
dc.identifier.doi10.1016/j.jhydrol.2024.132573por
dc.identifier.urihttp://hdl.handle.net/10174/38014
dc.language.isoengpor
dc.peerreviewednopor
dc.publisherElsevierpor
dc.rightsopenAccesspor
dc.titleImproving snowpack chemistry simulations through improved representation of liquid water movement through layered snow and rain-on-snow (ROS) episodes: Application to Svalbard, Norwaypor
dc.typearticlepor

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