Unraveling the Impacts of Snowpack Dynamics, Soil Properties, and Near-Surface Hydrology on Soil Temperatures and Biogeochemical Processes in Two Discontinuous Permafrost Watersheds
Authors
Ian Shirley1* (ishirley@lbl.gov), Chen Wang1, Zelalem Mekonnen1, Stijn Wielandt1, Sylvain Fiolleau1, Sebastian Uhlemann1, Katrina Bennett2, William J. Riley1, Baptiste Dafflon1, Colleen Iversen3
Institutions
1Lawrence Berkeley National Laboratory, Berkeley, CA; 2Los Alamos National Laboratory, Los Alamos, NM; 3Oak Ridge National Laboratory, Oak Ridge, TN
URLs
Abstract
In high-latitude permafrost environments, snowpack dynamics, soil properties, and near-surface hydrology are linked with soil thermal regimes, vegetation dynamics, and carbon cycling. Better understanding of these links requires collection of spatially and temporally dense hydro-geochemical data, which is made challenging by remote field locations and harsh environmental conditions. Here, the project uses spatially and temporally dense measurements of snowpack dynamics and soil temperature in conjunction with model experiments to explore the connections between snow, water, and carbon in two discontinuous permafrost watersheds.
The team collected co-located, vertically resolved time series of snow and soil temperature every 5 or 10 cm along 1.2 or 1.6 m probes between June 2021 and September 2023 at 82 locations in the Teller 27 watershed and 44 locations in the Kougarok watershed on the Seward Peninsula in Alaska. Researchers further perform one-dimensional model experiments using ecosys, a mechanistic, process-rich ecosystem model that has been successfully tested across high latitudes.
Variation in annual maximum snow depth is strongly correlated with mean annual soil temperatures at both sites. Researchers also find that interannual variability in snow regimes drives interannual variability in near-surface soil temperatures during the growing season. Modeled and observed snowpack dynamics (snow depths and snow temperature profiles) closely match observations throughout both years of data. A model sensitivity analysis shows that, in addition to snow depth, soil thermal-hydrological characteristics have a strong impact on simulated soil temperatures. This model result is used to explain why locations with similar snow depths can have different soil thermal regimes. The team further finds that spatial variation in snow depths is associated with variation in rates of biogeochemical processes (e.g., more primary productivity in areas of deep snow), and that interannual variation in snowmelt timing has a strong impact on growing season dynamics (e.g., leaf out date and growing season length).