Continuous:Snowfall Accumulation and Melting

When GSSHA is run in the LONG_TERM simulation mode, snowfall accumulation and melting is simulated to increase its utility in regions with significant snowfall. More accurate snow accumulation and melt algorithms as well as more accurate melt transport algorithms within GSSHA are active research and model development areas at ERDC. Snowfall has a large impact on hydrologic fluxes because snowfall is normally stored for a significant period of time in the snowpack and is later released as melt water. In many parts of the world melt of the snow cover is the single most important event of the water year (Gray and Prowse, 1993). Because snowfall accumulation and subsequent melting can have such a large influence in hydrologic response of a watershed, it is important to simulate these processes. The purpose of the snowfall accumulation and melting routine is to allow an accounting of these processes with the intent to differentiate between precipitation that is rainfall that will immediately infiltrate, pond and runoff or evaporate, and snow and ice that accumulates and significantly alters the timing of hydrologic fluxes.

All three methods used within GSSHA to simulate snow accumulation and melt assume that the snow pack consists of a single layer. Certain advantages - such as time variations of liquid water content (Bloschl & Kirnbauer 1991), interflow within the snow pack layers due to ice sheets, and avalanche modeling (Colbeck 1991) - do exist when applying multi-layer snow models, but the required data to accomplish such models on a watershed level is currently unrealistic in most basins. Multi-layer snow models are typically deployed at the site-scale where spatially-close data is available.

In nature snow is a distributed process. Because of the distributed structure of the GSSHA model the snow is modeled as a distributed process. Utilizing a distributed domain gives more potential of addressing a variety of real world problems than a semi-distributed or lumped-parameter model (Kirnbauer, Bloeschl et al. 1994). Because of the distributed domain, the model can also account for Orographic Effects.

Snow Related Inputs for GSSHA
Snow Card Inputs - Optional
Snow File Inputs - Optional

GSSHA Melt Algorithms
GSSHA currently employs three snow melt models:

 Energy Balance (EB)

 Temperature Index (TI)

 Hybrid Energy Balance (HY)

GSSHA Accumulation Algorithm
Independent of which snow melt model is deployed (viz. EB, HY, or TI), GSSHA has the capability to account for inaccuracies in the gage systems. Because gaging systems often underestimate the amount of precipitation fallen in the form of snow, two multiplication factors are applied to the gage measurement of precipitation (P_x) to create the adjusted precipitation of the newly fallen snow (P_n), Equation 6. The snow adjustment factor (SCF) is considered a calibration parameter while the fraction of precipitation in the form of snow (f_s) is considered constant at 1.0 when temperature are at or below 0° C, and 0.0 when above 0° C.

P_n = P_x * f_s * SCF

(6)

Typically when the air temperature is below 0° C precipitation falls in the form of snow. However, snow can form in air surface temperatures greater than 0° C. For this reason, the user is able to specify the temperature at which precipitation begins to fall as snow (MBASE, ° C). If MBASE is not specified within the project file it has a default value of 0° C. Anytime the air temperature is below MBASE during precipitation, the precipitation is assumed to be snow or ice that will accumulate on the land surface. If snow is already present in a cell, the new snow accumulation is added to the existing accumulated snow. While precipitation in the GSSHA model is distributed over the land surface, the effects of vegetation, elevation, and wind on the spatial distribution of snowfall are ignored.

If snowfall occurs, a warning will be printed to the screen and to the Summary file. When snow accumulation occurs the amount of snow in the watershed is reported at the beginning and end of each event summary in the Summary file.

GSSHA Melt-Water Transport Algorithms
Once melt occurs within the snow pack the subsequent melt-water is routed through the snow pack and then to other hydrologic processes within GSSHA (i.e. runoff, infiltration, evaporation, etc.) using melt-water transport (MWT) algorithms. GSSHA currently employs both a Vertical MWT and Lateral MWT algorithm.

A homogeneous snow pack assumption is utilized in GSSHA to alleviate computational and data limitation concerns associated with a heterogeneous assumption (which would include flow fingers). Equivalent properties for the homogeneous snow pack are often assumed (Colbeck 1979). In GSSHA, each cell has its own snow pack properties - namely hydraulic conductivity, saturation, and effective porosity - derived using the SNAP model (Albert & Krajeski 1998).

Flow is considered a porous medium, therefore a form of Darcy's Equation (http://en.wikipedia.org/wiki/Darcy%27s_law) is used to determine flux rates through the snow pack. Vertical MWT through the snow pack is considered unsaturated flow, while Lateral MWT between the ground surface and the bottom of the snow pack is considered saturated flow. To simulate the flow within the snow pack, accurately capturing the saturation within the pack is vital because the saturation affects both the hydraulic conductivity and effective porosity of the snow pack. GSSHA currently uses the SNAP model to determine the saturation, saturated / unsaturated hydraulic conductivity, and effective porosity of the snow pack in each cell.

GSSHA Snow Depth and Density
Snow depth and density are simulated in GSSHA independent of what melting algorithm is used. The depth and density of snow is calculated hourly by incorporating the SNAP model (Albert and Krajeski 1998) code into GSSHA through a symbiotic relationship. Information related to SWE, snow depth, density, snow saturation, effective porosity, and hydraulic conductivity are exchanged between the two models. GSSHA simulates the SWE, while SNAP simulates the remaining parameters.

The snow density within SNAP changes in response to snow accumulation, settlement, and melt, and is computed either with the snow depth predictions, or as updated by the user (Albert and Krajeski 1998). GSSHA currently does not allow the user to update the snow density as a calibration parameter, leaving the snow density to be calculated based on the snow depth predictions of SNAP with no calibrated parameters. For more information on the SNAP model the interested reader is encouraged to review Albert & Krajeski (1998). For more information on the depth prediction equations used within the SNAP model the interested reader is encouraged to review Anderson (1973), Jordan (1991), and Jordan (1998).

The snow depth simulations from Follum & Downer (2012) show that when the SWE is simulated accurately the SNAP model reasonably simulates the snow depth. The results show that the SNAP model may slightly overestimate snow depth when compared to observed data.