The quality of a groundwater model as a tool for sustainable management of our groundwater resources doesn´t on the quality of the input data, the accuracy of the calibration but also on the visualization of the output data and analysis of the water budget. There are several options to export contours from MODFLOW GUIs as Model Muse, however when analyzing several stress periods the graphical steps could be time consuming therefore a Python script would be helpful to export heads or water table as shapefiles.

Geopandas is an amazing library for spatial analysis since in combines the spatial tools from Shapely and Fiona with the versatily of Pandas Dataframes. We are aware that most geoscientists, water resources specialists and related professionals work on Windows, therefore we are always in the search of new ways to get Python working with all its geospatial capabilities in every computer. We have created a tutorial that shows the installation process of Geopandas and other Python geospatial libraries in Windows by the use of a Conda environment.

On the development of hydrological evaluations, there is a need to assess which weather stations will be used for the numerical modeling stage. The main acceptance criteria would be the amount of climate variables and records available however huge areas could have docens or hundreds of stations delivered in one text file. This tutorial shows the procedure to plot climate variable records from a single text file on a polar plot with aperture angle related to value statistics. The tutorial is done with standar Python packages as Matplotlib, Numpy and Pandas.

Evaluation of hydrological processes as evapotranspiration, runoff, routing and infiltration require data as precipitation, flow, temperature and radiation on a daily basis. Required data for hydrological modeling need to be accurate and must be completed over the study period. It is common that historical data from hydrological stations are incomplete and has many gaps that can be filled by the use of machine learning algorithms like Scikit-Learn in Python3. This tutorial shows a applied procedure to run a complete script for the filling of missing precipitation in one station by the use of data from 2 nearby stations. The script will be run on Python 3.9 on a Anaconda Prompt environment.

Raster reprojection is a common task on GIS analysis however to do it with only Python commands has some challenges. We have done an applied example of raster reprojection for single and multiple rasters from WGS 84 UTM to WGS 84 Geographic. The codes work on monoband and multiband rasters and can reproject from to any projection by specifiyin it EPSG code.

The tutorial is done on a conda enviroment that has the geospatial libraries installed. You can set up the environment by following this tutorial:

https://hatarilabs.com/ih-en/how-to-install-python-geospatial-libraries-gdal-fiona-rasterio-etc-under-a-conda-env-in-windows

Effective stress theory was developed by Terzaghi in the 1920s. Based on our modeling experience we wanted to calculate the effective stress based on the results from a MODFLOW groundwater model. Finally, after 6 years from the first thought about it we came with a full deduction of the effective stress calculation based on the model geometry and an applied example for the effective stress calculation on a hillslope groundwater flow model.

The example model was developed in Modflow-Nwt and Model Muse, whereas the effective stress determination was done with scripts in Python and Hataripy (our fork of Flopy). The scripts can also generate 3D objects as VTK files of the model results, geometry and stresses that can be visualized in Paraview.