You can work faster and have more time to analyze model results if the process of model creation takes less time. The design of the mf6Voronoi package is oriented to create an improved user experience by optimizing the options of mesh creation and by the implementation of Python script templates for meshing, model construction, and others. This is an applied case of groundwater modeling on a regional scale with Voronoi mesh built on MF6 Disv. The template can be easily customizable to other boundary conditions and will be the basis of coming tutorials and webinars.

This example develops a groundwater model in MODFLOW6 DISV that implements a Voronoi mesh generated from the basin boundary and river network with the mf6Voronoi packages that allow high performance meshing fully coupled with geospatial data. On the Voronoi mesh, the refinement levels are defined by a minimum cell size, maximum cell size and a multiplier. The applied case covers all steps on model discretization, construction, simulation and 2D visualization.

Groundwater modeling with several boundary conditions and complex hydrogeological setups require advanced tools for mesh discretizacion that ensures adequate refinement in the zone of interest while preserving a minimal cell account. Type of mesh has to be engineered in a way to preserve computational resources and represent adequately the groundwater flow regime.

We have developed an applied case of groundwater model discretized from ESRI Shapefiles with refinement areas. Boundary conditions are also set up from spatial data with the intersect functionality of Flopy. Model surface and layer bottom are imported / processed from xyz point data. The simulation is run for one steady  and ten transient stress periods and results are plotted for head on aerial view and cross section.

In order to improve the accuracy of a groundwater flow simulation we need a strong conceptual model, a high quality observation dataset and a numerical code that can handle specific characteristics of the groundwater flow we want to evaluate. MODFLOW 6 can deal with variable density flow with the BUY package and also with variable viscosity with the VSC package that is implemented on the latest versions of Model Muse. This tutorial covers an applied case of variable viscosity modeling due to the injection of salinity and heat to a model with regional groundwater flow. The main features for the implementation of the VSC package are explained and the resulting concentrations on two observation points are evaluated with Flopy codes.

Groundwater model creation requires a complete set of spatial data for the different hydraulic parameters, boundary conditions and other model items. Vector and raster data need to be preprocessed, converted, reprojected to fit the requirements of Model Muse. 

This tutorial covers an applied case of raster and vector data processing for a basin.

Python has awesome packages for machine learning that can be coupled to groundwater models and perform automatic calibration of hydraulic parameters. This tutorial covers the procedure to implement a neural network based on a set of parameters set with corresponding head values; the study case is on a transient pumping test model with an observation point located around 10 meters away from the pump well. The analysis predicts the calibration parameters from the spatially interpolated observed head values. 

We did this tutorial since the documentation or examples on the topic were not available. Flopy can export the model grid and model attributes to shapefile with a coordinate system of reference for the three types of discretizations of MODFLOW 6. We have done an applied example to export the grid of a discretized by vertices model (DISV) to the ESRI Shapefile model. The tutorial also show options for the final grid representation in geopandas.