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Snow Hydrology and Cold Regions Engineering


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 Rainbow in blue sky


Observed and simulated snow cover in 2006

Distributed snow modeling with topography and canopy effect
We have developed a snow energy balance algorithm that can compute the snow state very efficiently for distributed model. This spatially distributed snow model was named as Dsnow model. This snow model explicitly incorporates the vegetation cover effect on snowmelt process. An example model output in the Feather, Yuba, American River watersheds, CA, the US, was visualized in left figure. More applications of this model can be found in the publications: Ohara and Kavvas (2006), Chen et al. (2011a), Ohara et al. (2013).


Simulated direction of wind map of Wyoming

Numerical weather model for atmospheric forcing
The dynamic downscaling technique using a numerical weather prediction (NWP) model can effectively meet the data requirements of the process-based snow model even in ungauged or sparsely gauged basin. The NWP model can also quantify the historical wind and snowfall field in Wyoming for better snow fence system design. The simulated historical wind field is shown in right graphic. The numerical weather model was used for physically-based probable maximum precipitation (PMP) estimates (Ohara et al. 2011). One of series papers on this topic, Ishida et al. (2015), was awarded the American Society of Civil Engineers (ASCE) J. James R. Cross Medal 2016, the second highest research paper award in the ASCE.

Snow density and moisture measurements; Trench and gutter for hillslope measurements

Field study of snowmelt runoff process
Ohara et al. (2012) performed a field study at the northwest sector of the Ward Creek watershed, Lake Tahoe Basin. They observed the significant overland/in-snow flow even over the unfrozen unsaturated topsoil of a snow-covered hillslope. This overland/insnow flow that can amplify the peak flow discharge may be a more common phenomenon than has been considered on a snow-covered hillslope. This finding was verified by another field experiment in Snowy Range, Wyoming.

Snow redistribution modeling
Snow depth imageOhara et al. (2014) generalized the snow movement equation for watershed scale applications by incorporating snow transport, wind snow diffusion, and snow gravitational movement. This new formulation can explicitly describe the snow diffusion process by wind turbulence. The proposed formulation was tested through the model simulation using a 10-m digital elevation model in Muddy Gap, Wyoming, as shown right. We try to understand the blowing snow processes using the theoretical and field approaches.


Snow drift theory chart

Snow drift prediction
Snowdrift process was formulated as a linear partial differential equation based on Eulerian framework (Ohara, 2017). Ohara (2017) showed that most of the particle deposit patterns behind an object can be characterized by the particle motion parameters: diffusion, drift, and erosion coefficients (schematic right, from Ohara 2017). This new formulation can improve understanding of particle distribution patterns found in the fields of riverbed erosion, sedimentology, aeolian landforms (e.g. ripples and dunes), as well as snowdrift.

Inter-annual snow modeling

Inter-annual snow modeling
Energy and mass balance model for terrestrial ice and snow is the most powerful tool for future projection of inter-annual snow storage. However, the snow models in hydrologic engineering field cannot handle the long-term snow storage due to lack of snow movement parameterization (schematic left, from Ohara et al. 2014). We have been working on an appropriate treatment for inter-annual snow including inland glaciers as systems in dynamic equilibrium that stay constant under a static climate condition.

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Civil and Architectural Engineering and Construction Management

EN 3074

Dept. 3295

1000 E. University Ave.

Laramie, WY 82071

Phone: (307)766-2390


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