Modeling groundwater flow on a regional scale has its own challenges because a regional model itself deals with refinement requirements, larger baselines, higher differences in elevations, complex geologies and areas without observation data.

The applied model of this tutorial covers the Angascancha Reservoir basin groundwater flow model in Ayacucho, Peru. This example have been modeled in several courses, tutorials and webinars with Modflow 2005, Flopy and Model Muse, however, this is the first time we port the model (succesfully) to Modflow 6 and Model Muse 4.

Great free and open groundwater flow modeling software are in constant development process, therefore year by year we have newer versions or even new software. This time we have uptated a basic and useful tutorial from 2016 to the new version of Modflow and Model Muse. The tutorial shows the complete procedure to download and install Model Muse 4 and configure Modflow 6.

We have explored the new (or not so new) options of Modflow 6 and Model Muse 4 to create local refinements on a regional scale based on hydraulic features. So far, we came up with a decent example of regional groundwater modeling on a andean basin with three boundary conditions and decreasing hydraulic conductivity with depth. The tutorial also explores the complexities to model basins with high difference on elevation and the use of text defined parameters to implement the Newton formulation in Modflow 6.

Finite difference method as well as any other discretization method allows the conceptualization of a geological media into cells or other volumes. Geological models come in diverse formats in binary or text format and need to be “translated” to the cell extension of a groundwater model.

This tutorial has a applied example of the implementation of a 3D geological model from a neural network into a groundwater model with determined horizontal discretization and layer thickness. The tutorial covers all the steps for model construction and hydrogeological unit determination with scripts in Python with Flopy and other libraries. Comparisons of the original and translated geological model were done as Matplotlib plots and Vtk files in Paraview.

We have done tutorial in Python and recent and powerful libraries as Scikit Learn to create a geological model based on lithology from drillings on the Treasure Valley (Idaho, USA). The tutorial generates a point cloud of drillings lithologies that are transformed and scaled for the neural network. The selected neural network classifier is Multi-layer Perceptron classifier implemented on the Scikit Learn library as sklearn.neural_network.MLPClassifier. An analysis of the confusion from the neural network is performed. The tutorial also includes a georeferenced 3D visualization from well lithology and interpolated geology as Vtk format in Paraview.

Conceptualization and simulation of groundwater flow in the area of influence of geological faults is really a challenge for numerical modelers. Besides the complexities and variety of faults there were some limitations of the numerical codes to simulate the mixed behaviour of faults and altered zones. We have developed an example tutorial to represent the main characteristics of groundwater flow in the fault zone for an applied case. The tutorial is developed in Model Muse with the MODFLOW DISV option for unstructured grid generation. Groundwater flow was analyzed on the water balance and a particle tracking simulation was done with MODPATH 7.

The process and options to define the cell / layer confinement has changed in MODFLOW 6 with respect to MODFLOW 2005. We have done an explanatory video about the process to setup the confinement for cells in MODFLOW 6 with Model Muse. There is also a discussion on the influence of the confined / convertible option on the aquifer response on an example model.

The disconnexion of surface groundwater flow it is fact very common on highly exploited aquifers or under impacts from climate change. Many rivers are disconnected from the groundwater flow regime decreasing their flow amount due to infiltration losses. For the simulation of this surface flow, unsaturated flow and groundwater flow phenomena the MODFLOW package Streamflow Routing (SFR) was developed.

This tutorial develops an numerical model of disconnected surface groundwater flow with MODFLOW SFR and Model Muse. The study case has regional flow and a river network with monthly flow on transient conditions over 2 years.

Hydraulic parameters for an aquifer flow can be expressed on different terms like Hydraulic Conductivity (K) or Transmissivity (T). On the default setup of MODFLOW 2005 with Model Muse there is no option to use directly Transmissivity unless a manual conversion is done to transform into K, this is because the selected flow package is Layer Property Flow (LPF).

When the flow package changes to Block Centered Flow (BCF6) it is possible to insert Transmissivities directly as datasets or as a object property. The BCF package also has more options for the layer type, with unconfined and partial confined aquifer options.

Of the latest devopments in groundwater modeling there are two softwares: Modflow 6 and Model Muse 4. both developed by the USGS. The first software is the latest version of MODFLOW that allows triangular and unstructured grids, and the second is the latest version (from June 23) of the graphical user interface Model Muse that supports Modflow 6.

Unstructured grid it a type of discretization that allows us to have small cells at certain parts of the model while the rest of the model has bigger cells. This optimization of the model grid and cell number decrease the computing time, the size of the output files and the speed of the visualization tools. With unstructured grids we can model a great extension while preserving the right accuracy of the points of interest, we can even insert regional faulting or complex geological setups.

This tutorial shows the complete procedure to create a geospatial model of a alluvial aquifer with the interaction of regional flow, river and wells. The tutorial creates the unstructured grid, boundary conditions, model geometry, simulate flow with MODFLOW 6 and represent results in Model Muse 4.

Cities under high exploitation of groundwater resources face severe problems of land subsidence due to pumping. This complex problem was addressed from earlier versions of MODFLOW as MODFLOW 2000 and has two packages: MODFLOW SUB and  for its simulation with different options for the conceptualization and numerical modeling of normal aquifers and low conductivity interbeds.

Scientific research and professional consulting on the field of land subsidence modeling is limited; this scarcity might be because the coupled complexity of the appliance of numerical groundwater modeling and consolidation theory.

Nature is geospatial, and every physical process related to the groundwater flow and transport regime is spatially located or spatially distributed. Groundwater models are based on a grid structure and models are discretized on cells located on arrangements of rows and columns; is that level of disconnexion of the spatial position of a piece of porous media and the corresponding cell row and column that creates some challenges for the sustainable management of groundwater resources.

We have to create or re-create the duality in between the geospatial and the model grid, that would be similar to duality of a vector GIS object and its metadata on its essence but more difficult to manage. Affortunately Flopy, the Python library to build and simulate MODFLOW models, has tools to georeference the model grid even with rotation options. The workflow is kind of explicit, meaning that the modeler need a medium knowledge of Python and Flopy tools. This tutorial shows the whole procedure to create a fully geospatial groundwater with MODFLOW and Flopy.

On the MODFLOW groundwater model construction process in Model Muse there are tools to import hydrogeological parameters as hydraulic conductivity and boundary conditions from shapefiles assigning the parameter values from the spatial object metadata. This type of workflow is useful for idealized aquifers or simple hydrogeological assessments; however on groundwater models at regional scale the calibration process requires the quick and repetitive change of the hydrogeological parameters and boundary conditions to evaluate the hydraulic response against observed data.

For model calibration the use of Global Variables (Data/Edit Global Variables...) of Model Muse makes relatively easy, ordered and with better change control the calibration process. This tutorial gives the procedure to implement Global Variables for the hydraulic conductivities of 15 hydrogeological units distributed on a model of 560 square kilometers.

Groundwater modeling on the regional scale or with huge baseline can be tedious to do manually in a Graphical User Interface as Model Muse. There are more advanced options to insert a great amount of wells with long and diverse pumping schedules with Flopy or by altering the *.gpt file with some programming effort; however these are kind of intermediate level solutions that would be time expensive for a beginner groundwater modeler.

This tutorial shows the procedure to insert multiple wells with different pumping rates in MODFLOW with Model Muse by the use of some special features. The procedure can insert wells at different depths however it cannot set the well name and in case of wells with multiple pumping records a group of superposed wells will be inserted, one for each pumping rate record.

Groundwater modeling with MODFLOW and other codes are defined as inverse modeling where the aquifer parameters can be calculated from the comparison of model results with observed data. This comparison process is time consuming, employs acceptance criteria and trend analysis of the boundary condition influence.

There are tools for the comparison of observed and simulated heads in Model Muse and custom charts can be done with few lines in Python. This tutorial cover the whole procedure to create a simulated / observed plot in Python from the results of a MODFLOW model run on Model Muse. The study case is over a regional model with more than 100 piezometers. The tutorial creates a graph with a colorbar and exports it as a JPG file.