Model Muse version 5 has been released, now with PEST support.

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A major version of Model Muse was released last May 18, 2022 after more than a year from the previous release. This version has some change as the capacity to choose the creation of model archives, options to select objects by name, supports adaptive time stepping for MODFLOW 6, have additional support for MODFLOW packages but most importantly it has capabilities to run PEST for all its functionalities: prediction, regularization and Pareto.

Model Muse is capable of running Pest simulations on Modflow 2005, Sutra and Modflow 6 created with Model Muse and other types of Modflow 6 as discretized by vertices and unstructured grid models on Modflow 6.

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How to make a geospatial Rest Api web service with Python, Flask and Shapely - Tutorial

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Geospatial analysis is not limited to a single desktop software or a python kernel; if you use massive spatial analysis or if you work on a team that want some specific spatial output the use of a Rest Api might be convenient. We have developed a basic, introductory but clear tutorial of a geospatial Rest Api that implements the Post method and Get method and returns the centroid of a polygon and the list of elements with their coordinates respectively. Tutorial is done in Windows, however a real Rest server that runs several high energy spatial queries is expected to run in Linux.

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Modeling Land Evolution at Basin Scale with Python and Landlab - Tutorial

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Climate changes, people change and land also changes with time. We can`t believe that the river networks will remain the same over the next 1000 years or that mountains and depressions will have the same elevation in the next 10000 years. But changes are not related to huge time frames, they can occur in decades or years at lower rates as well. In order to evaluate those changes we need some formulation of the key components of land evolution: fluvial, hillslope and uplift. We have developed a tutorial with Python and the Landlab library to simulate the land evolution at basin scale for 100 thousand years; inputs come from geospatial rasters and output data is exported as Ascii raster files.

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How to export MODFLOW head contours as Shapefiles with Flopy - Tutorial

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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.

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VOCs and PFAs Interactive Spatiotemporal Representation with Python and Folium - Tutorial

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Analysis of groundwater chemistry is a difficult task for the limited set of monitoring samples, the limited samples and the limited analized components. In order to assess the actual extension of a contamination plume or the efficiency of remediation techniques we need new and innovative methods to plot and analyze water chemistry data with open source data. We have done an applied case of interactive VOCs and PFAs representation on a Jupyter notebook with Python, Folium and Ipywidgets. The dataset has more than 3300 samples of 127 points over a period of 30 years and corresponds to a contaminated site of a former airfield.

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Mplleaflet vs Folium to plot your weather stations on a Jupyter Notebook

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When analyzing climate data or any other geospatial data on a Jupyter Notebook, we need to plot points, lines and polygons interactively. Based on our own way to learn Python, most probably we will use a library that we found on Google or Stackoverflow however there are some undercover issues on the library selection that we will discuss in this article.

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How to install Python - Geopandas in Windows on a Conda Environment - Tutorial

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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.

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Radial plots for exploratory analysis of climate data with Python and Matplotlib - Tutorial

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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.

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Applied case of hydrogeological conceptual modeling on a hillslope area with QGIS

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In order to understand the relationship between groundwater flow and slope stability, a study area over a hotspot in the US. Landslide Inventory was selected. The geology, groundwater wells, elevation, and climate conditions were reviewed to develop a hydrogeological conceptual model that will be the basis for a numerical model of the groundwater flow regime. This webinar deals with the review of available spatial data related to the groundwater flow regime on a hillslope area at Mukilteo city, Washington, US. Information from geology, meteorology, observation data and boundary conditions will be discussed in a QGIS session with regards to the construction of a groundwater numerical model in MODFLOW.

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Data extraction and spatial / 3D representation from BGS borehole data in AGS format with Python

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Open lithology datasets are scarce and most times they come in certain exchange formats that can´t be easily coupled with other softwares. Python has the tools to read and extract data from those files and provide them in more friendly formats as csv or xlsx. With the use of spatial libraries as Fiona or 3D visualization libraries as Pyvistas we can go one step further and process our data as shapefiles or vtks.

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Two open geological modeling softwares that you should know about

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For geologists, hydrogeologists, geostatistics, petroleum engineers, and other related professionals the choice for 3D geological modeling software was related to expensive and restrictive software that was in fact a “de facto” choice in several companies and institutions.

Although it is a choice of any company or professional to select the software anyone will use to model the geological units, a great gap subsides (using a geological term) on the use of this expensive software. If the software is expensive, how expensive will it be to get trained on this software? If few people have skills with certain software, how easy would it be to change to another software? How people can assess the quality of one software if they have no full capability on managing several softwares. As you have seen, restrictions on the licenses lead to these subsiding gaps, faults on quality, and an intrusion of professional ignorance.

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The best Youtube channels in QGIS (and open source GIS tools)

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Education is aimed to acquire knowledge and it's based on the learning process and as any process has some steps and means that have to be analyzed. Traditionally, if you want to get knowledge you can go to a university, get a degree, visit a library, but why don’t you just check a Youtube channel; will you get the same knowledge? Will you do better research with what you learned on video? Those are excellent questions to pose in these times where we need knowledge that shapes our future in the context of climate change.

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How long does it take to develop a groundwater model for my research project / master thesis?

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Developing a hydrogeological research project that considers groundwater modeling for a bachelor thesis or a master thesis requires you to take into consideration several topics, such as input data, hydrogeological knowledge and time to learn the software in order to provide a numerical simulation that reaches the objectives of your research project.

Learning a software for groundwater modeling should be easy as developing some tutorials and playing around with your own data; however it is not that easy since it involves the review of lots of documentation, have strong knowledge in hydrogeology and numerical modeling and deal with uncertainties and numerical unconvergences.

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Fill Missing Precipitation Data with Machine Learning in Python and Scikit-Learn - Tutorial

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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.

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The Groundwater Project as a source of free books in basic and applied hydrogeology

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We usually received some emails and messages regarding basic or reference books and publications in Hydrogeology and we didn't have any reference of free and high quality material until we knew about the Groundwater Project. It´s true that we found the project website some months ago, but it was at the latest IAH conference where we had a presentation with a complete overview of the program.

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Example of Seawater Intrusion Modeling with Modflow 6, Model Muse and Flopy - Tutorial

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MODFLOW 6 implements the Buoyancy package for the simulation of variable density and seawater intrusion cases. The tools are implemented in the Python package Flopy, however the workflow has changed substantially from the previous models of flow and transport. We have developed an applied case seawater intrusion model on a regular geometry model constructed with Model Muse for flow and Flopy for transport.

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What to do when your MODFLOW MT3D contaminant plume doesn’t migrate?

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This is a problem that every hydrogeologist - numerical modeler has faced many times over his/her professional career independently from the theoretical background and skills with the code, graphical user interface or Python library.

We don’t know why groundwater “flows” and a contaminant plume “migrates” when both move on the aquifer system. After you construct you model geometry, setup the flow boundary condition, specify your transport package, define your contaminant sources, run your MODFLOW flow model and then run your MT3D model (or even your MT3DMS model) you will find that there is no plume, none, nada, nichts.

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Open source groundwater software besides MODFLOW - Updated July 2023

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A special concern arises from the gap between the stage of development of open source groundwater software and the actual capabilities of the common groundwater modelers (we mean, the main group of modelers). Even though we are passionate about software we don’t find the time or the right momentum to have a real experience with those codes.

From our research, we have listed the top open source groundwater software with a brief summary of the capabilities and useful links. We have to mention that there are some packages that can simulate groundwater flow in software like OpenFoam; however, those variants are not listed here.

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Is glTF an alternative to VTK for the representation of voronoi mesh Modflow6 models?

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Representation of geospatial objects in 2D are well covered by the GIS software and standards where we have powerful tools and interchangeable spatial files, however when we work on 3D objects the universe of software and tools is like a jungle with a wide variety of issues. We will always consider 3D as the ultimate representation of an object using a computer, by sure the holograms and augmented reality can improve the way we interact with a object geometry, but since we are not in the common near future we will give much effort to the representation of these objects in our computer screens, tables and cellphones.

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