Analysis and Modeling of Frozen Ground and Soil Temperature in North Dakota
Publication: World Environmental and Water Resources Congress 2021
ABSTRACT
Long periods of snow-covered frozen ground affect the generation of surface runoff by altering the infiltration process. Physically based approaches to identify frozen ground are relatively data-intensive, since they incorporate heat transfer procedure for computing the energy flux of the soil. In order to simplify the identification of frozen ground, the majority of the macro-scale hydrologic models utilize empirical frost indices to estimate the frozen ground condition. The main objectives of this study are to provide statistical analyses of the frozen ground condition and to evaluate the performance of a continuous frozen ground index (CFGI) methodology by using measured soil temperature data in North Dakota. The observed average daily air temperatures and snow depth data from the snow data assimilation system (SNODAS) were used to estimate the frozen ground condition for different stations. The analysis of the soil temperature data and results from the CFGI method accentuate the presence of long periods of frozen ground conditions at different stations. Results from this study can be used by macro-scale hydrologic models to provide better simulations of generated surface runoff in cold climate regions.
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World Environmental and Water Resources Congress 2021
Pages: 545 - 551
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© 2021 American Society of Civil Engineers.
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Published online: Jun 3, 2021
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