Tupac Cloud is our developing online groundwater modeling platform. Models on Tupac Cloud are based on Voronoi meshes and constructed in MODFLOW 6 DISV from Flopy scripts. The whole project will have lot of tools and integrations with QGIS and Paraview, this is just an example of mesh generation for an applied case of river aquifer interaction.
Read MoreOver the last decade Model Muse has been on the most complete and open source pre and post processing tools for groundwater modeling with Modflow. Latest developements on Model Muse are related to the implementation of new tools in MODFLOW 6, but there are also enhacements related to previous MODFLOW versions, PHAST and Sutra. The most relevant changes on this version are:
Read MoreQuadtree refinements are available for Modflow 6 models in the latest versions of Model Muse. This tool can be inplemented for many spatial objects as points, lines or polygons where you can specify the refinement level for each layer separately. We have done an applied case of mesh refinement of a regional model where the river network, observation points and tunnel aligment were defined as refinement objects.
Read MoreSetting up information for several stress periods on normal GUIs tools can be an exhausting since it implies opening/formatting/filling/closing many charts manually. This task becomes more complicated if the model needs to be updated every given time. We wanted to develop a case where a steady state model can be updated to a transient model from data of a spreadsheet file.
This tutorial cover the implementation of a transient model of a river/well/aquifer interaction where the number of stress periods, the length of stress periods, time steps, multiplier, steady/transient mode, pumping rate, river stage and output mode are updated from a excel (.xlsx) file. The tutorial is divided into 3 steps where each part shows sequential parts of this model transformation.
Read MoreModel Viewer for Modflow 6 was launched in April 2022 and it is a version of Model Viewer customized to display the features, grid and results of MODFLOW 6 models. We have done an applied case for the visualization of a MODFLOW 6 DISV model that has a voronoi mesh. The tutorial covers the steps to visualize the model grid lines, grid shell, isosurfaces, color grid, flow directions among other visualization options.
Read MoreWe are allways on the research of new visualization tools for groundwater models and that would imply the review of different apis, codes and backends. This time we have done an applied example for the interactive generation of cross section from a Modflow 6 model with a voronoi mesh on a Jupyter notebook. The cross section shows the model grid, head distribution as a color grid and contours and its generated from the line delineation over the 2d view of the model grid. The code can be easily adapted to any Modflow model by editing the Python and Flopy code.
Read MoreWe have developed a version of our Angascancha basin case study that runs on MODFLOW 6 with discretized by vertices (DISV). The model implements a Voronoi mesh that is generated from the basin boundary and river network and the refinement levels are defined by a minimum and maximum cell size together with determined refinement steps. The tutorial is complete in all the steps involving the model discretization, construction, simulation and visualization.
Read MoreIf you have a set of drone imagery and you want to know the location and direction of the camera, this tutorial might be interesting for you. We have done an applied example that retrieves the geospatial metadata from the drone camera for a group of images and makes a geostapatial repretacion on a map with the image name available as popup. The tutorial is done under a Jupyter notebook with Python and Folium.
Read MoreIn the semantic space the forbidden word is related to refuse to allow, and thus can create a controversy with the freedom of hydrogeologist / numerical modelers to choose whichever tool that can make the work done or the simulation run. However we as specialists in one part of the dynamics of the subsurface are conscious that solutions can not and must not be evaluated by single criteria.
Read MoreThis is an applied example of a fully geospatial groundwater flow model with the MODFLOW 6 Disv discretized by vertices option. Model was constructed with Python and Flopy from a series of shapefiles and it has a progressive refinement for well and river boundary conditions. Regional flow has been simulated as a General Head Boundary and plotting options were developed for the grid and model output representation on areal view and cross section.
Read MoreWe have to redefine the way we do our geospatial analysis. It is not enough to know the "tool" but to master the "process" if we want to provide spatial solutions for the always bigger geospatial data available. We have done a simple process of extracting vertices from polygons, lines and points inside a polygon in Python, but not a simple and declarative script but as a Python class.
Read MoreOn the research of performance tools for spatial analysis we took a chance on exploring Julia for applied raster algebra together with the ArchGDAL package. This tutorial shows the complete procedure to load rasters in Julia, plot them, extract the band arrays, calculate the NDVI index and export the resulting array as a geospatial raster.
Read MoreOn the exploration for advanced tools for spatial analysis we took a chance on making raster algebra with R and the Terra package tutorial. This is an applied example of NDVI calculation from the red and near infrared bands from a Landsat 8 image. The script covers all steps from band load, array algebra and export as geospatial raster.
Read MoreWe believe that tasks / studies that involve the massive analysis of vector and raster spatial data would become more popular in the near future. With massive analysis some factors became more relevant such as the computer memory, processor type, operating system and the programming language or platform used for the calculation.
We make a case study of raster analysis in Windows where the Python and R were installed with their spatial packages from conda repositories. Our computer skills don't allow us to have a sense of the differences in the performance of these scripts in Linux.
Read MoreWe have researched advanced tools as programming languages for spatial analysis, so far Python was our de facto choice because of its learning rate and easy to use. However, when working with a high amount of data and performing massive spatial queries some questions arise about the performance of Python and then we look at some other options as R, Julia or Rust. We have made a tutorial for the installation of the advanced spatial package Terra on a R kernel in Jupyter under a Conda environment, the tutorial covers all the installation steps in Windows together with some examples to load and plot vector and raster data.
Read MoreA 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.
Read MoreGeospatial 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.
Read MoreClimate 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.
Read MoreThe 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.
Read MoreAnalysis 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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