Groundwater Modeling System (GMS)
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GMS is the most sophisticated and comprehensive groundwater modeling software available!
The Groundwater Modeling System (GMS) is the most sophisticated and comprehensive groundwater modeling software available! Used by thousands of people at U.S. Government agencies, private firms, and international sites in over 90 countries, it has proven to be the modeling system of choice for groundwater professionals. GMS provides tools for every phase of a groundwater simulation including site characterization, model development, calibration, post-processing, and visualization. GMS supports both finite-difference and finite-element models in 2D and 3D including MODFLOW 2000, MODPATH, MT3DMS, RT3D, SEAM3D, UTCHEM, FEMWATER, PEST, MODAEM, SEEP2D and UTEXAS. Regardless of your modeling needs, GMS has the tools!
In this overview:
- Groundwater Flow & Transport Options
- GIS-based Model Conceptualization
- 3D Model Conceptualization
- Site Visualization
- Risk Assessment (Stochastic) Modeling
- Automated Model Calibration
Groundwater Flow & Transport OptionsThe variety of modeling options in GMS is unparalleled! Rather than being limited to one main model (such as MODFLOW) and accompanying “add-on” codes, GMS provides interfaces to a wide range of 2D or 3D models. Here is a brief overview of the options available to you:
2D Flow
- Perform fast, easy modeling with the MODAEM analytic element model integrated into GMS!
- 2D finite-element seepage modeling is supported in the SEEP2D model – perfect for dams, levees, cutoff trenches, etc.
3D Flow
- 3D finite difference modeling with MODFLOW 2000 (saturated zone)
- 3D finite-element modeling with FEMWATER (saturated and unsaturated zone)
Solute Transport
- Simple analytical transport modeling with ART3D
- Simple 3D transport with MT3D, MODPATH, or FEMWATER
- Reactive 3D transport with RT3D or SEAM3D
- Multi-phase reactive transport with UTCHEM
Unsaturated Zone Flow and Transport
- Fully 3D unsaturated/saturated flow and transport modeling with FEMWATER or UTCHEM
GIS-based Model Conceptualization
One of GMS’s greatest strengths is the conceptual model approach. Since 1996 when GMS first introduced the Map module, users have been building models conceptually and independently of their numerical grid. This approach makes it possible to build a conceptual model using GIS feature objects (points, arcs, and polygons). The conceptual model defines the boundary conditions, sources/sinks, and material property zones for a model. The model data can then be automatically discretized to the model grid or mesh. The conceptual model approach makes it possible to deal with large, complex models in a simple and efficient manner.
The GIS Module now available in GMS has made creating conceptual models from GIS data even easier. With direct linkage to ArcGIS and almost any format of GIS data, you can access geometry and attributes faster than ever before.
Whether the GIS data is created in GMS or imported from GIS files, the method of model building remains the same. You edit the model at a GIS object level and let GMS do the hard work of grid or mesh building and parameter assignment to each element of the model.
3D Model Conceptualization
GMS has advanced tools for the creation of complex 3D stratigraphy models and the ability to translate that 3D object directly to a finite-difference grid model or finite-element mesh model.
The “Horizons” approach allows you to create complex solids from borehole and cross section data quickly and easily. These tools allow you to create solids with complex stratigraphy such as pinch out zones, truncations, and outcroppings.
You can transfer the results (material properties) of a solid model direclty to a numerical model such as a MODFLOW grid or a FEMWATER mesh. You can also direclty generate MODFLOW 2000 HUF data – GMS is the only system that allows you to do this!
Site Visualization
GMS is a powerful graphical tool for model creation and visualization of results. Models can be built using digital maps and elevation models for reference and source data. During the model building process, the graphical representation of the model allows quick review and presentation of your work. Fully 3D views, with contouring and shading, of your model allow anyone to see and understand the domain and parameters of your analysis.
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A groundwater model can be displayed in plan view or 3D oblique view, and rotated interactively. Cross-sections and fence diagrams may be cut arbitrarily anywhere in the model. Hidden surface removal, and color and light source shading can be used to generate highly photorealistic rendered images. Contours and color fringes can be used to display the variation of input data or computed results. Cross-sections and iso-surfaces can be interactively generated from 3D meshes, grids, and solids, allowing the user to quickly visualize the 3D model.
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Both steady-state and transient solutions can be displayed in an animated format (as if viewing a movie) using either vector, iso-surface, color fringe, or contour animation. For example, animation of a transient solution allows the user to observe how head, drawdown, velocity, and contaminate concentration vary with time. In addition, GMS can also sweep an iso-surface through the 3D model. The minimum and maximum iso-surface values are determined from the model and the program will then linearly interpolate and display multiple iso-surfaces in rapid succession. This allows the user to quickly understand the spatial variation of a contaminant plume, for example.
Risk Assessment (Stochastic) Modeling
One of the most exciting features in GMS is a suite of tools for performing stochastic simulations with MODFLOW and accompanying transport models.
The Risk Analysis Wizard is a new tool associated with the stochastic modeling tools in GMS. Two types of analysis are currently supported: probabilistic threshold analysis and probabilistic capture zone delineation. This wizard allows you to quantify the risk of a contaminant exceeding critical levels in groundwater or the risk of a capture zone including key areas at a site. Such analysis helps determine appropriate action to be taken in design or remediation.
Two approaches are supported for setting up stochastic simulations: parameter randomization and indicator simulation. The parameter randomization can be done using either a “Monte Carlo” or a “Latin Hypercube” approach. The indicator simulation approach randomizes the spatial distribution of the parameter zones using the T-PROGS software. The T-PROGS software is used to perform transition probability geostatistics on borehole data. The output of the T-PROGS software is a set of N material sets on a 3D grid. Each of the material sets is conditioned to the borehole data and the materials proportions and transitions between the boreholes follows the trends observed in the borehole data. These material sets can be used for stochastic simulations with MODFLOW.
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Automated Model Calibration
Calibration is the process of modifying the input parameters to a groundwater model until the output from the model matches an observed set of data. GMS includes a suite of tools to assist in the process of calibrating a groundwater model to point and/or flux observations. When a computed solution is imported to GMS, the point and flux residual errors are plotted on a set of calibration targets and a variety of plots can be generated showing overall calibration statistics. Most of the calibration tools can be used with any of the models in GMS.
Automated parameter estimation is supported in GMS for the MODFLOW simulations using MODFLOW PES, PEST, and UCODE. These are sometimes called “inverse models”. Most of the steps involved in setting up an inverse model in GMS are the same regardless of the selected inverse model. The basic process for inverse modeling is:
- Build a base model with MODFLOW
- Input observed data (point or flux data)
- Indicate the model input parameters that the inverse model can adjust to make the model match the observations.
- Let the inverse model run – it will adjust input parameters and run the MODFLOW simulation repeatedly until the best match betweeen computed data and observed data is obtained.
