Difference between revisions of "Continuous:Snowfall Accumulation and Melting"
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| − | + | Snow melt is the single most important event of the water year in many parts of the world (Gray and Prowse, 1993). Snow accumulation and melt are both spatially heterogeneous, thus distributed domains often give more potential for addressing a variety of real world problems (Kirnbauer, Bloeschl et al. 1994). When GSSHA is run in the '''LONG_TERM''' simulation mode, snowfall accumulation and melt are automatically simulated, with the hydrologic response being a combination of the multiple other processes (infiltration, overland flow, groundwater, etc.) utilized within GSSHA. GSSHA employs multiple snow model options to use depending on available data and the objective of the study. These models will be discussed below. | |
| − | + | Because of the distributed domain, the model can also account for '''[[Orographic Effects]]'''. | |
Within GSSHA, the snow pack consists of a single layer in each cell. While multi-layer models have 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) - the required data to populate model 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. The methods used in GSSHA require only the standard precipitation, Chapter 6, and hydrometeorological data, Chapter 9.5, required for any long term continuous simulation in GSSHA, and are readily available from a variety of sources. While optional inputs are possible, the model can produce accurate results with no additional inputs other than standard precipitation and hydrometeorological inputs. The methods currently used in GSSHA to simulate snow accumulation and melt can be found here [[media:Snow_TN.pdf|Follum and Downer 2013]] <br> | Within GSSHA, the snow pack consists of a single layer in each cell. While multi-layer models have 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) - the required data to populate model 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. The methods used in GSSHA require only the standard precipitation, Chapter 6, and hydrometeorological data, Chapter 9.5, required for any long term continuous simulation in GSSHA, and are readily available from a variety of sources. While optional inputs are possible, the model can produce accurate results with no additional inputs other than standard precipitation and hydrometeorological inputs. The methods currently used in GSSHA to simulate snow accumulation and melt can be found here [[media:Snow_TN.pdf|Follum and Downer 2013]] <br> | ||
Revision as of 13:43, 17 March 2017
Snow melt is the single most important event of the water year in many parts of the world (Gray and Prowse, 1993). Snow accumulation and melt are both spatially heterogeneous, thus distributed domains often give more potential for addressing a variety of real world problems (Kirnbauer, Bloeschl et al. 1994). When GSSHA is run in the LONG_TERM simulation mode, snowfall accumulation and melt are automatically simulated, with the hydrologic response being a combination of the multiple other processes (infiltration, overland flow, groundwater, etc.) utilized within GSSHA. GSSHA employs multiple snow model options to use depending on available data and the objective of the study. These models will be discussed below.
Because of the distributed domain, the model can also account for Orographic Effects.
Within GSSHA, the snow pack consists of a single layer in each cell. While multi-layer models have 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) - the required data to populate model 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. The methods used in GSSHA require only the standard precipitation, Chapter 6, and hydrometeorological data, Chapter 9.5, required for any long term continuous simulation in GSSHA, and are readily available from a variety of sources. While optional inputs are possible, the model can produce accurate results with no additional inputs other than standard precipitation and hydrometeorological inputs. The methods currently used in GSSHA to simulate snow accumulation and melt can be found here Follum and Downer 2013
Snow Related Cards for the GSSHA Project File
No additional input cards are required to simulate snow accumulation, melt, and meltwater routing. The default is for precipitation to be added to the snowpack anytime the air temperature, as specified by the hourly input values, is below 0 C. Melt occurs according to the hybrid energy balance method, as described below. Flow through the snow pack and to the overland occurs in waves. Overland routing will ignore the snow pack. All of these defaults can be altered as described below.
Snow Card Inputs - Optional
GSSHA Melt Algorithms
GSSHA currently employs three snow melt models:
Energy Balance (EB)
Temperature Index (TI)
Hybrid Energy Balance (HY) Default
Adjustment of Atmospheric Radiation Fluxes within the Snow Model
GSSHA currently employs methods to account for both longwave and shortwave radiation in each cell. Longwave radiation is mainly a function of temperature, clouds, and atmospheric emissivity, while the shortwave radiation calculations take into consideration albedo, topographic shading, aspect of the terrain in relation to the sun, albedo of snow as it ages, atmospheric absorption and reflection, clouds, and vegetation. The calculated longwave and shortwave radiation values are then used within the EB and HY models to simulate the melting of snow.
In order to account for the Effects of Shading and Aspect on the available energy to melt snow, GSSHA employs a method that reduces the amount of energy available to melt snow when the Energy Balance or Hybrid Energy Balance snow melt algorithms are used. The Effects of Shading and Aspect are set as a default within GSSHA and are only used for snow fall, but are currently being examined in the Evapo-Transpiration methods as well. Shading and Aspect angles are not accounted for when raster-based HMET data is input into the model (http://www.gsshawiki.com/Distributed_HMET_Data).
GSSHA Accumulation Algorithm
Independent of which snow melt model is deployed (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 (Px) to correct the precipitation of newly fallen snow (Pn), Equation 6. The snow adjustment factor (SCF) is considered a calibration parameter while the fraction of precipitation in the form of snow (fs) is considered constant at 1.0 when temperature are at or below 0° C, and 0.0 when above 0° C.
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Pn = Px * fs * 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.
Within a grid cell, 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 depth, density, 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 grid cell. Water is routed to the overland through the snow pack in a series of waves calculated from these parameters.
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. Information related to SWE, snow depth, density, snow saturation, effective porosity, and hydraulic conductivity are exchanged between the two models. The SNAP model algorithms are used to calculate all the parameters except SWE.
According to the SNAP formulation, the snow density 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 the SNAP alorithms 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 Follum and Downer 2013.
Overland Routing with Snow
During overland flow routing, as described in Section 5.2, GSSHA ignores snow in an overland flow cell unless the user specfies to route the flow through the snow using Darcy's law with the project card ROUTE_LAT_SNOW. Routing through the snow as free surface flow may cause simulated flows to be higher and arrive earlier than measured flows strongly influenced by the snowpack. When this card is specifed, the flow in cells with a snowpack will be computed using Darcy's law. The default is to use the SNAP calculated vertical hydrualic conductivity (K) for computation of flow through the snow in the lateral flow computations. The alternative is to specify the lateral hydraulic conductivity with the SNOW_DARCY card, which is followed with a value of hydraulic conductivity (m/s). References report hydraulic conductivities of snow on the order of 1 cm/s (0.01 m/s) (Colbeck and Anderson, 1982 for example). The current implementation of the SNAP model produces simliar values but as of v6.2 the implementation of SNAP in GSSHA is considered experimental and is not currently reccomended and it is reccomended that the user specify the lateral hydrualic conductivity of the snow using the SNOW_DARCY card.
GSSHA User's Manual
- 9 Continuous
- 9.1 Computation of Evaporation and Evapo-transpiration
- 9.2 Computation of Soil Moisture
- 9.3 Hydrometeorological Data
- 9.4 Snowfall Accumulation and Melting
- 9.5 Sequence of Events During Long-Term Simulations
