https://doi.org/10.1016/j.jhydrol.2017.05.042

Krogh, S. A., Pomeroy, J. W., & Marsh, P. (2017). Diagnosis of the hydrology of a small Arctic basin at the tundra-taiga transition using a physically based hydrological model. Journal of Hydrology, 550, 685-703. https://doi.org/10.1016/j.jhydrol.2017.05.042

A better understanding of cold regions hydrological processes and regimes in transitional environments is critical for predicting future Arctic freshwater fluxes under climate and vegetation change. A physically based hydrological model using the Cold Regions Hydrological Model platform was created for a small Arctic basin in the tundra-taiga transition region. The model represents snow redistribution and sublimation by wind and vegetation, snowmelt energy budget, evapotranspiration, subsurface flow through organic terrain, infiltration to frozen soils, freezing and thawing of soils, permafrost and streamflow routing. The model was used to reconstruct the basin water cycle over 28 years to understand and quantify the mass fluxes controlling its hydrological regime. Model structure and parameters were set from the current understanding of Arctic hydrology, remote sensing, field research in the basin and region, and calibration against streamflow observations. Calibration was restricted to subsurface hydraulic and storage parameters. Multi-objective evaluation of the model using observed streamflow, snow accumulation and ground freeze/thaw state showed adequate simulation. Significant spatial variability in the winter mass fluxes was found between tundra, shrubs and forested sites, particularly due to the substantial blowing snow redistribution and sublimation from the wind-swept upper basin, as well as sublimation of canopy intercepted snow from the forest (about 17% of snowfall). At the basin scale, the model showed that evapotranspiration is the largest loss of water (47%), followed by streamflow (39%) and sublimation (14%). The models streamflow performance sensitivity to a set of parameter was analysed, as well as the mean annual mass balance uncertainty associated with these parameters.

https://doi.org/10.1016/j.jhydrol.2017.05.042