ModelMuse is a graphical user interface (GUI) for the U.S. Geological Survey models MODFLOW 6 (Langevin and others, 2017, Provost and others, 2017, Hughes and others, 2017), MODFLOW-2005 (Harbaugh, 2005), MODFLOW-NWT (Niswonger and others, 2011), MODFLOW-OWHM (Hanson and others, 2014), MODFLOW-CFP (Shoemaker and others, 2007), MODFLOW-LGR (Mehl and Hill, 2005, 2007, 2010), SUTRA (Voss and Provost, 2002), WellFootprint (Winston and Goode, 2017), and PHAST version 1 (Parkhurst and others, 2004). All the various versions of MODFLOW are three-dimensional finite-difference or finite volume (MODFLOW 6) groundwater models. They simulates steady and nonsteady flow in an irregularly shaped flow system in which aquifer layers can be confined, unconfined, or a combination of confined and unconfined. MODFLOW-LGR adds local grid refinement to MODFLOW. MODFLOW-NWT provides an alternate method for solving problems involving drying and rewetting nonlinearities of the unconfined groundwater-flow equation. MODFLOW-CFP adds methods for simulating conduit flow, MODFLOW-OWHM, combines features of MODFLOW-2005, MODFLOW-NWT, and MODFLOW-LGR and also adds methods for water management in a water allocation context. PHAST simulates multi-component, reactive solute transport in three-dimensional saturated groundwater flow systems. SUTRA uses the finite element method in three dimensions to simulate groundwater flow and transport of either heat or solutes. WellFootprint (Winston and Goode, 2017) is used for visualizing groundwater withdrawals.
ModelMuse is based on GoPhast (Winston, 2006). ModelMuse allows the user to define the spatial input for the models by drawing points, lines, or polygons on top, front, and side views of the model domain. These objects can have up to two associated formulas that define their extent perpendicular to the view plane, allowing the objects to be three-dimensional. Formulas are also used to specify the values of spatial data (data sets) both globally and for individual objects. Objects can be used to specify the values of data sets independent of the spatial and temporal discretization of the model. Thus, the grid and simulation periods for the model can be changed without respecifying spatial data pertaining to the hydrogeologic framework and boundary conditions. The points, lines, and polygons can assign data set properties at locations that are enclosed or intersected by them or by interpolation among objects using several interpolation algorithms. Data for the model can be imported from a variety of data sources and model results can be viewed in ModelMuse. This report describes the basic operation of ModelMuse along with an example model. Additional information and examples are provided in the ModelMuse help system, which can be accessed from the Help menu.
Once the model has been defined in ModelMuse, the user can create the input files for the model by selecting File|Export and then export either the MODFLOW or SUTRA input files or the PHAST transport input files. The user has the option to execute the model immediately once the input files are exported.
In cases where the input files for MODFLOW-2000 and MODFLOW-2005 are identical, it may be possible to use ModelMuse to create input files for MODFLOW-2000. However, ModelMuse has not been extensively tested with MODFLOW-2000. Some differences between MODFLOW-2000 and MODFLOW-2005 include the formats of the input files for the observation process and the absence of the Unsaturated Zone Flow (UZF) package in MODFLOW-2000.
The current version of ModelMuse does not support all the options in MODFLOW. Additional options and other programs may be supported in future versions of ModelMuse.
ModelMuse stores all its data in a single file. Several file formats are supported. Of these, the most commonly used are text files with the extension ."gpt" and compressed binary files with the extension ."mmZLib."
In ancient Greece and Rome, the Muses were thought, by some, to provide the inspiration for music, poetry, and the arts. The composers, poets, and other artists, however, still had to do the hard work of turning that inspiration into an actual work of art. It would be great if ModelMuse could do the same for modelers—provide the key insight required to allow the system to be quickly and effectively modeled. ModelMuse cannot do that; it is not smart enough. What it can do, however, is take over some of the mundane parts of the modeling process and make them much easier and faster. By doing so, ModelMuse allows the modeler more time to think, to observe, to analyze, to experiment, and to generate the needed inspiration.
