Which are the challenges for model calibration in MODFLOW 6?

MODFLOW 6 is the latest version of MODFLOW; we can’t frame this version as new because it has been released in May 2017 when QGIS 3 wasn’t even released or Python 3.6 was the latest version. On the hydrogeological perspective changes don't come as fast as expected and the amount of users of MODFLOW 6 is increasing but there are limitations on the pre/post processing tools that can take the best of the software for the general public.

Besides the philosophical discussion if a model can be “calibrated” or just “validated” there are some changes on the structure of MODFLOW 6 that bring challenges on the traditional way of model calibration.

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Online Voronoi Modflow 6 groundwater modeling with Hakuchik - Tutorial

Because a new year has come with a new way to model groundwater flow using the latest developments of Modflow 6 Disv. We are not exactly sure that we have done a tutorial, or a procedure or a bunch of code and tools together but we are sure that we have done something that can model geospatial groundwater flow online with awesome 3d representations. At the time we launched all the code on the Hakuchik server, we had a feeling of "we did it" and you are entitled to see if our work is good, bad or in progress. We are sure that more development needs to be done but this will be the way of groundwater modeling in the future, hoping that improving the capacities of professionals and concern of the general population will lead to a sustainable management of our groundwater resources.

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Variable density groundwater flow modeling with MODFLOW 6 and BUY package - Tutorial

Coastal aquifers and the interaction among brine / fresh groundwater need to be evaluated with a modeling code that can deal with variable density flow. For more than 18 years, SEAWAT was the prefered (or only) open source solution implemented in Modflow 2000 with some limitations* on its use with Flopy. Now in Modflow 6 the concept of simulation involves flow and transport modeling together with exchange among them. We have done a tutorial with a simple case of variable density flow from a saline lake into an acuifer. The transient model has a duration of 50 days where the saline water "intrudes" the aquifer at the bottom part of the lake.

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Hatarilabs presents its study area for integrated and sustainable groundwater management

Because field work and numerical models are just tools for the sustainable management of our groundwater resources we have searched a study place to develop a long-term project on a high-stressed aquifer that will lead to sustainable and effective groundwater policies. The study area is an alluvial aquifer that was primarily used for agriculture production with an neighboring unplanned urban development that has increased the domestic / industrial water demand. Several pumping schemas were implemented on the area and have affected the groundwater levels and the water availability for agriculture.

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How to geolocate drone imagery from a csv table with Python and Piexif - Tutorial

If your drone doesn´t write the GPS position on the image metadata, this is a tutorial that might be of your interest. When you have the images without any location reference and the image location on another text file you can use the code described below to generate geolocated drone imagery compatible with OpenDroneMap. The tutorial shows all the steps involved besides it has some sample data to practice.

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How to create a geospatial Raster from XY data with Python, Pandas and Rasterio - Tutorial

Another tutorial done under the concept of “geospatial python”. The tutorial shows the procedure to run a Scipy interpolation over a Pandas dataframe of point related data having a 2D Numpy array as an output. With some procedures of Rasterio the Numpy array was transformed into a monoband geospatial Tiff raster.

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How to join lines and densify vertices with Python, Fiona, Shapely - Tutorial

We have done a tutorial under the concept of "applied geospatial Python". This is an example that deals with a selective filtering of a determined road from a road geopackage. The selected road is composed of a group of lines that are merged into a Shapely LineString. Based on a Numpy linspace with the Shapely interpolate function, a set of points were distributed along the merged line path and later interpreted as a LineString. Resulting line was saved as a ESRI Shapefile file with Fiona.

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Convert from Excel Spreadsheet to Point Shapefile with Python, Pandas & Fiona - Tutorial

We have done a tutorial with a workaround procedure in Python with Pandas and Fiona of a common and but multi-step process to create point shapefiles from excel spreadsheets. The process involve some lines of code to read the excel file, define the output shapefile structure and write the point data.

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Machine Learning Supported Groundwater Model Calibration with Modflow, Flopy, PySal and Scikit Learn - Tutorial

We have done a tutorial on a low-level-complexity model with rivers, lakes, recharge and regional groundwater flow done in Model Muse in a previous tutorial. The model was imported as an object in Python with Flopy. A sensibility analysis was done with SALib to assess the response for the object model groundwater flow to a different sample of parameters and a resulting set of parameters and corresponding heads (parameters -> heads) were recorded. Then a machine learning regression was performed with Scikit-Learn with the inverse set (heads->parameters) to get the predicted parameters for the observed data. Different error measurements were performed for two model cases to assess the overall quality of the neural network regressor.

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How to set a multiuser Jupyterlab server with Jupyterhub (in Windows with Docker)

When teaching or working with Python there is a challenge to have and work simultaneously with the same Python version and Python packages. Jupyterhub is a great solution to bring notebooks to a group of users with admin tools and many more features. We have done a tutorial to install Jupyterhub with Jupyterlab in a local lan or wifi network. The tutorial shows the procedure to run Jupyterhub from Docker in Windows, set up a native authenticator and configure an admin and users.

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How to do a simulated groundwater head difference map in MODFLOW and ModelMuse - Tutorial

This is a tutorial about some functionalities of Model Muse to analyze and process layer heads or parameters. The applied example was done based on a previous tutorial of a model with 3 stress periods, one static and two transient periods of 10 days. The simulated map shows the head difference in the last 10 days.

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How to clip polygon layers with Python, Fiona and Shapely - Tutorial

This tutorial shows the entire procedure to clip a polygon layer to an area of interest in Python with the use of spatial libraries as Fiona and Shapely. The tutorial opens the polygon and clip layer as fiona elements, interpret the geometries as shapely Polygon datatypes, clip the polygons and store results as an output shapefile with the corresponding metadata.

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Review of the latest functionalities of the new release of MODFLOW 6 (mf6.2.0)

Modflow6 is the latest version of the USGS code for groundwater modeling MODFLOW. This version has been coupled with another USGS open source software as Model Muse and Flopy and can implement diverse spatial discretization structures on a complete rearrangement of packages and keywords. We feel that there is a gap on the capabilities from a common groundwater modeler to adopt all the new development on Modflow and other open source groundwater software therefore we have prepared a video with the summary of this new release.

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How to clip Sentinel 2 bands to an area of interest with Python, Rasterio and Fiona - Tutorial

Under the concept of “Python spatial” we have developed a tutorial for the spatial processing of multiple bands from a Sentinel 2 image. The tutorial shows the procedure to read the set of bands, import a shapefile, clip each band and export the clipped version in another folder. The spatial process is independent from raster resolution and can be easily modified for Landsat images.

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How to set a Multi Aquifer Well (MAW) in MODFLOW 6 and Model Muse - Tutorial

Modeling the dynamic of the groundwater flow related to pumping was a challenge with the traditional Wel package in MODFLOW since wells are screened on the most part of the well depth crossing multiple layers of a groundwater flow model. The Multi Aquifer Well (MAW) package came as a solution for this complex behaviour of layer interaction in Modflow. Model Muse with Modflow6 can implement the MAW package with ease.

This tutorial shows the complete procedure to set up a MAW well on a 3 layer groundwater flow model, gives an overview of the package options and represents the well heads and well rates with scripts in Flopy.

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Geospatial triangular interpolation with Python, Scipy, Geopandas and Rasterio - Tutorial

Under the concept of “applied geospatial Python” we have developed some procedures / tutorials of some common spatial analysis tasks done on desktop GIS software. The aim isn’t to reinvent the wheel but to explore the current Python tools and libraries that can create, analyze and represent both vector and raster spatial data.

Triangular interpolation is one of several types of interpolation techniques available in both Python and GIS software, however the advantage of working with Python is that the interpolation is a function where you can get the interpolated value on a specific point while in GIS software you are required to create a raster and sample values from the raster (.. as far as we know).

We have created a tutorial with a complete procedure in Python to import points with elevation as a attribute, creates a triangular interpolation function and has two spatial outputs: an interpolated geospatial raster in TIFF format and a shapefile with elevation attribute for another set of points. The tutorial uses several Python libraries as Matplotlib, Rasterio, Geopandas, Scipy.

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Geospatial crop counting from drone orthophotos with Python, Scikit Learn and Scikit Image

Orthophoto from drones provide us aerial imagery with spatial resolution in the scale of centimeters. With this high definition and cheap ortophotos we can interpret, analyze and quantify objects on a horizontal distribution by means of machine learning libraries for image recognition and cluster analysis. We have done an applied example of plant recognition and counting from a drone orthophoto with Python and the machine learning libraries Scikit Learn and Scikit Image. The whole process is geospatial, as it works with raster and shapefiles and results are finally displayed on QGIS.

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Basic tutorial of geological modeling with Python and Gempy

Gempy is an open-source library for modeling geology written in Python. The library is capable of creating complex 3D geological models including structures, fault networks, and unconformities and it can be coupled with uncertainty analysis.

We have created a tutorial of geological modeling based on geological contacts and surface orientations. The tutorial was developed in a Gempy container that runs under Docker in Windows 10; the tutorial covers the software installation and the geological modeling scripting.

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A comprehensive list of specific Python packages for hydrogeology and groundwater modeling

Development of open source software brings amazing new tools in all fields. In hydrogeology and groundwater modeling there is an increasing number of specific open source software and programming packages. We wanted to compile the latest libraries for Python related to hydrogeology, we asked for references and researched over the web to provide you the following list.

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Extract point value from a raster file with Python, Geopandas and Rasterio - Tutorial

This tutorial has a complete case of spatial analysis for the extraction of point data from a raster dataset with Python and its libraries Geopandas and Rasterio. The procedure is entirely geospatial and uses shapefiles and tifs as input data; data calculation was performed on a Jupyter Lab environment.

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