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Endalamaw, Abraham; Bolton, W. Robert; Young-Robertson, Jessica M.; Morton, Don; Hinzman, Laryy; Nijssen, Bart (2017)
Languages: English
Types: 0038
Modeling hydrological processes in the Alaskan sub-arctic is challenging because of the extreme spatial heterogeneity in soil properties and vegetation communities. However, modeling and predicting hydrological processes is critical in this region due to its vulnerability to the effects of climate change. Coarse spatial resolution datasets used in land surface modeling poised a new challenge in simulating the spatially distributed and basin integrated processes since these datasets do not adequately represent the small-scale hydrologic, thermal and ecological heterogeneity. The goal of this study is to improve the prediction capacity of meso-scale to large-scale hydrological models by introducing a small-scale parameterization scheme, which better represents the spatial heterogeneity of soil properties and vegetation cover in the Alaskan sub-arctic. The small-scale parameterization schemes are derived from observations and fine resolution landscape modeling in the two contrasting sub-basins of the Caribou Poker Creek Research Watershed (CPCRW) in Interior Alaska: one nearly permafrost-free (LowP) and one that is permafrost-dominated (HighP). The fine resolution landscape model used in the small-scale parameterization scheme is derived from the watershed topography. We found that observed soil thermal and hydraulic properties – including the distribution of permafrost and vegetation cover heterogeneity – are better represented in the fine resolution landscape model than the coarse resolution datasets. Parameters derived from coarse resolution dataset and from the fine resolution landscape model are implemented into the Variable Infiltration Capacity (VIC) meso-scale hydrological model to simulate runoff, evapotranspiration (ET) and soil moisture in the two sub-basins of the CPCRW. Simulated hydrographs based on the small-scale parameterization capture most of the peak and low flows with similar accuracy in both sub-basins compared to the parameterization based on coarse resolution dataset. On average, small-scale parameterization improves the total runoff simulation approximately by up to 50 % in the LowP sub-basin and 10 % in the HighP sub-basin from the large-scale parameterization. This study shows that the proposed small-scale landscape model can be used to improve the performance of meso-scale hydrological models in the Alaskan sub-arctic watersheds.

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