Hydrology Modeling in Alaska: Model Documentation Template

(please fill out as much as possible)

Your name:Elchin Jafarov

Model name:GIPL-2.0

Authors:V. Romanovsky, G., Tipenko, S. Marchenko

Source code location (if public):

Citations and URLs for basic documentation:

Tipenko, G., Marchenko, S., Romanovsky, V., Groshev, V., and T. Sazonova, (2004). Spatially Distributed Model of Permafrost Dynamics in Alaska, Eos Trans. AGU, 85(47), Fall Meet. Suppl., Abstract C12A-02.

Marchenko, S., Romanovsky, V. & Tipenko, G. (2008). Numerical Modeling of Spatial Permafrost Dynamics in Alaska. Proceedings of the Ninth International Conference on Permafrost, University of Alaska Fairbanks, Jun 29 - July 3, 2008, 2: 1125-1130.

Sergueev, D., G. Tipenko, V. Romanovsky, and N. Romanovskii, 2003: Mountain permafrost thickness evolution under influence of long-term climate fluctuations (results of numerical simulation). In: Proceedings of the VII International Permafrost Conference, Switzerland, July 21-25, pp. 1017-1021.

Source code language:FORTRAN

For the following section, you may wish to use appropriate keywords such as:

Physically-based, statistical, lumped parameter, spatially distributed, transitive model, equilibrium model, implicit, semi-implicit, explicit, TOPMODEL based, finite element, finite differences, routing, bottom boundary condition, parallel code, Richardson equation, optimization, forecast, etc

Model type and/or conceptual framework:

The GIPL-2.0 model simulates soil temperature dynamics and the depth of seasonal freezing and thawing by solving 1D non-linear heat equation with phase change numerically. In this model the process of soil freezing/thawing is occurring in accordance with the unfrozen water content curve and soil thermal properties, which are specific for each soil layer and for each geographical location. Special Enthalpy formulation of the energy conservation law makes it possible to use a coarse vertical resolution without loss of latent heat effects in phase transition zone even in case of fast temporally and spatially varying temperature fields. At the present stage of development, the GIPL model is combined with ArcGIS to facilitate preparation of input parameters (climate forcing from observations or from Global or Regional Climate Models) and visualization of simulated results in a form of digital maps. The input data are incorporated into GIS and contains the information on geology, soils properties, vegetation, air temperature, and snow distribution

Data needed to run the model (inputs):

Surface air temperature, precipitation, initial distribution of soil temperature with depth, prescribed soil physical properties (porosity, water content, thermal conductivity, heat capacity), lower boundary conditions (heat flux, geothermal gradient, temperatureor zero heat flux).

Parameters and how they are derived:

Thermal conductivity, heat capacity, soil water/ice content,soil porosity, and freezing point depressionare prescribed parameters.

Spatial element used to lump inputs and outputs:

Spatially distributed regular grid

Sub-models (i.e. snow or ground thermodynamics):

Snowproperties (depth, density, and thermal conductivity) simulation from the snow water equivalent.

Rainfall/runoff transformation mechanism:

N/A

Runoff routing within spatial elements and to basin outlet:

N/A

Method for including sub-grid scale processes:

N/A

Resolution (possible & prudent):

Any, but prudent is not finer than 5-10 m

Method of deriving topography:

N/A

Calibration approaches:

With the temporal approach, the quality of the modeling series is assessed by time series regression against measured data. The quantitative relationship between simulated and measured data is then determined for a 'calibration' period with some instrumental data withheld to assess the veracity of the relationship with independent data.

In the spatial approach, assemblages of the observed data from a number of different geographic locations with different landscape settings determine the quality of the modeling results. To achieve geographic correspondence between the scale of observation and modeling, we utilized a regional-scale permafrost characterization based on observations obtained from representative locations.

Treatment of frozen ground:

Numerical simulation including unfrozen water content calculation

Publications using this model:

Strengths and Weaknesses in Alaska applications:

The model solved using unconditionally stable numerical scheme, high accuracy. Daily resolution output data such as ground temperature through the entire soil column, active layer thickness, unfrozen water content, time of freeze-up. The soil characterization used in the GIPL-2.0 model is based on extensive empirical observations, conducted in representative locations that are characteristic for the major physiographic units in Alaska.

But it is stand alone model. Soil thermal properties and soil water content are prescribed parameters and are constant during the entire of simulation time.