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.

Pumping well productivity depends of the aquifer porous media, aquifer thickness and extension, physical processes of water cycle relevant to groundwater flow regime, well design and operation considerations. Determination of maximum pumping rate from a well is the key for well construction process because the maximum rate is relevant to pump sizing, licensing, purchase of conduction materials and dimension of water storage facilities. On a project feasibility perpective, the maximum rate estimation is important to meet the project demand on its different stages.

This tutorial shows the complete procedure to determine the maximum pumping rate from a well on a quaternary aquifer on steady state flow conditions with MODFLOW and Model Muse. The tutorial covers all the steps of spatial and temporal discretization, boundary condition setup and hydraulic parameter definition, with an emphasis on the conceptualization of a maximum pumping scenario with operational conditions on the well.

Gempy is as open source Python library for generating full 3D structural geological models. The library is a complete development to create geological models from interfases, faults, and layer orientations, it also relates the sequence of geological layers to represent rock intrusions and faults order.

Algorithm for geological modeling is based on universal cokriging interpolation with the support of high-end Python mathematical libraries as Numpy, PyMC3 and Theano.

Gempy creates a grid model that can be visualized as 2D sections with Matplotlib or as 3D geometrical objects as VTK objects that allow the representation of the geologic models on Paraview for custom slicing, filtering, transparencies, and styling.

This tutorial is a basic example of a stratified geological setup with 5 layers and one fault. In order to make the tutorial fully accessible to the majority of users, we have created a complementary tutorial about how to install Gempy on Windows with a repository distribution of Anaconda.

One of the most exceptional new features from MODFLOW 6 is the different discretization options for the model mesh generation. Options range from regular grid (same as MODFLOW 2005), triangular mesh and unstructured grid. Flopy that is a Python library for the build and simulation of MODFLOW 6 and other models has tools for triangular mesh generation. The workflow on groundwater modeling with MODFLOW 6 and Flopy for triangular mesh models is pretty fluid and we see a lot of potential for local and regional groundwater flow modeling.

This tutorial shows the complete process to create a triangular mesh with the utilities from Flopy and incorporate it to a MODFLOW 6 model. The model is simulated and results are represented as colored mesh and contour lines.

Regional groundwater modeling is an important task on a strategic water management that involves all users, activities, and involved ecosystems and provides a sustainable use for current and future conditions. There are some specific considerations on the regional modeling with respect to baseline and spatial discretization, a regional model is not intended to provide the aquifer response for a determined area, instead it involves the assessment of the regional groundwater flow and the quantification of the recharge, discharge and other process on the water balance.

This tutorial is the Flopy / MODFLOW numerical example of the Angascancha basin. The example is on steady steady and is solved with the NWT solver. Model output representations have been done under the Flopy/Matplotlib tools as well as some Python code to create VTU files and styled on Paraview.

SEAWAT is a model developed by the USGS for the simulation of three-dimensional variable density groundwater flow with solute and heat transport. The software is based on MODFLOW-2000 and MT3DMS and on its latest version it can simulate viscosity variations and provide faster execution times. SEAWAT is implemented on Flopy, the Python library to build, run and represent MODFLOW models. This tutorial has the complete workflow to create and represent a basic example of saline instrusion with SEAWAT and Flopy on a Jupyter Notebook.

Groundwater flow direction representation is useful to understand the actual and predicted conditions of the groundwater flow regime. The arrow direction and magnitude give a quick perspective of the main groundwater flow directions and the interconexion between sources and discharge points. This tutorial show the complete workflow to determine the flow directions from a MODFLOW model done with Model Muse. The scripting insert a background image, georeference the model from parameters exported as comments, and export the resulting figure as a PNG file. The tutorial is done in Python 3 on a Jupyter Notebook.

Karst systems are characterized by underground drainage systems formed by the dissolution of soluble rocks. The behaviour of these systems is hard to be conceptualized due to the uncertainty in the location and geometry of these underground caves and its connection with the porous media.

In order to be able to model these systems, the Conduit Flow Process (CFP) package (developed by the United States Geological Survey – USGS) can simulate turbulent ground-water flow by coupling the groundwater flow equation with formulations for a discrete network of cylindrical pipes.

The following tutorial explains how to set up a simple karst conduit system in a previously existing MODFLOW model and the analysis of the results.

Making hydrogeological models can take a long time, from construction, visualization of results and calibration. It is important to use tools that can optimize these tasks and allow the time saved to be used in the analysis of the system.

In this opportunity we will use Flopy to replicate a 2D transport model from a previous post. Flopy is a versatile set of Python scripts which can be used to run MODFLOW and MT3D, amongst other MODFLOW-related groundwater programs in a simple and efficient way. It will be seen how useful this tool is to automate the process of creating groundwater models since modifications of the boundary conditions can be done just by changing the text file.

In addition, MT3D-USGS will be used for the transport modelling. It is an updated release of the groundwater solute transport code MT3DMS, which has new transport modeling capabilities that provide a greater flexibility in the simulation of solute transport and reactive solute transport.

Aquifers can be porous, fractured or karstic. Due to the geological setup and processes related to the formation of the porous media, these acuifers can present a high degree of heterogeneity that affect/impact the groundwater flow patterns and contaminant transport and distribution.

Contaminant plumes for puntual/aerial sources interact with aquifer heterogeneities and anisotropies. Understanding and conceptualizing the distribution of the different hydrogeological units and their properties on the subsurface is a challenge for hydrogeologist, numerical modelers and remediation specalists.

MT3D-USGS is the one of the latest software for contaminant transport developed by the USGS. The initial release was on 2016 as a updated release of MT3DMS. The software has new capabilities for transport modeling coupled with the current MODFLOW packages, it can model unsaturated-zone transport, reactions and remediation schemas.

This transport modeling code is implemented in the pre and postprocessing software for groundwater modeling Model Muse, also developed by the USGS. This tutorial show a basic example of contaminant transport from a point source in a groundwater flow regime controlled by regional flow and discharge to ponds and rivers.

Seawater intrusion is an issue in coastal aquifer management especially on arid environments. Overexplotation of groundwater resources by high production wells on intensive irrigation schemes could lead to an intrusion of saline water from sea and an impact to the quality of water sources.

Available options to assess the behaviour and impact of seawater intrusion are limited in both open source and commercial software; there is also a need of highly skilled groundwater modelers to understand the complex model setup and model output that can provide useful information about the groundwater flow regimen and the risks to water quality posed by seawater intrusion.

Seawater Intrusion Package 2 (SWI2) is a modeling software developed by the USGS and coupled on MODFLOW-2005. SWI2 allows three-dimensional variable-density groundwater flow and seawater intrusion in coastal aquifer systems. This tutorial deals with a basic example of the implementation of SWI2 on a MODFLOW model contructed on Model Muse. The tutorial show the whole procedure of model setup, datasets implementation, conceptualization of boundary conditions and result evaluation.

Assessing the groundwater flow regime in coastal aquifers is a challenge for numerical modelers. In order to have a complete set of parameters and input data for a valuable numerical simulation we need to compile several hydrogeological studies, reconstruct datasets and proof the accuracy of hydraulic tests. Still some parameter would be missing but the experience and criteria of the groundwater modeler would achieve adequate simulations and predictions required by an adaptive sustainable groundwater resources management.

Aquifer modeling requires understanding and expertise on the different boundary conditions available to represent the physical process related to the groundwater flow regime on determined spatial and temporal discretizations. MODFLOW has a set of boundary conditions of specified head, specified flux and mixed. There is a particular boundary condition created for the representation of the interaction between a river and the surrounding aquifer: the River Package (RIV). This tutorial show the implementation procedure of a River Package (RIV) on a regional model with a discussion on the model output and water balance.

On a normal groundwater modeling workflow the hydraulic parameters, observed data and boundary conditions are preprocessed on a GIS software as QGIS, and then imported on a compatible format (vector or raster) into the modeling software. However, Model Muse has a set of different tools to process point, and tabular data into model parameters increasing the speed in the model construction and simulation. This tutorial show the procedure to interpolate hydraulic conductivity from a table into Modflow from a different set of interpolation methods in Model Muse.

MT3DMS, a three-dimensional transport model, will be used in this tutorial to simulate two-dimensional transport in a radial flow field. The example consists on a well which is injecting a solution in a constant rate of 100 m3/d with a contaminant concentration of 10 g/m3 (10 mg/l). This model will run for a total of 27 days.

The following tutorial will explain how the model was build and the conditions considered.