Lastest developement in Tupac Cloud for the representation of head as contours and color grid. Both representations have a colorbar and title related to selected layer. Objects as boundary conditions and model mesh can be represented on the plots.
Read MoreOur first tutorial of voronoi mesh groundwater modeling with Tupac Cloud. The study case deals with the river - aquifer interaction with wells and general head boundaries. The model has confined and convertible layer and two stress periods (steady and transient). The model is run on the platform with a view of the Modflow simulation report. Water budgets and heads are analyzed on different tables, plots and maps.
Read MoreFrom the latest developments in our codes, MF6 and Flopy we have created tools in Tupac Cloud for 3d mesh generation as Vtk files. This is an applied example of this functionality based on a dataset for river - aquifer interaction where the resulting Vtk are plotted on Paraview.
Read MoreTupac 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 MoreHatarilabs presents its educational program designed for mastering Python in real professional and academic environments. The program has an extensive practical work that goes from the basic concepts of Python, Numpy and Pandas to specific applications in water resources and geosciences coupled with geospatial analysis and machine learning.
We have designed a complete program on MODFLOW with Model Muse that ranges from the essential topics in flow and transport modeling to applied cases of regional modeling, infrastructure projects, and seawater intrusion. MODFLOW with Model Muse is a recent open-source software that provides various tools for simulating the groundwater flow regime in different environments.
This program is designed to provide systematical learning from the most basic and general topics to intermediate and advanced topics, allowing the student to understand the various steps in developing hydrological models using the SWAT+ interface for QGIS. This program has a series of theoretical and practical concepts that allow the student to process spatial data, manage meteorological databases, build hydrological models, develop sensitivity analyses, and perform manual and automatic calibrations using versatile tools such as SWAT+ Toolbox, SQLite, and Python.
ONLINE COURSES:
Phreeqc is a software developed by the USGS written in C++ capable of modeling a variety of aqueous geochemical processes such as mixing of waters, modeling equilibrium between solid and aqueous phase, modeling impact of temperature, calculation of element concentration among others.
Hatarilabs has developed an applied and unique course of Phreeqc integrated with Python undes a class (object type) that can run, parse and provide Phreeqc output as Pandas dataframes and integrate it to plots and further analysis with other Python libraries on a Jupyter notebook.
HEC-RAS is a software designed to perform hydraulic 1D and 2D calculations on river channels either natural or man constructed. It allows steady flow, unsteady flow, sediment transport and water quality modelling. It is developed by the US army corps of engineers, US Department of defence and it is openly available software. The HEC-RAS steady flow is based on one dimensional energy equation, while for the unsteady flow, HEC-RAS uses an implicit, finite difference method to solve the fully dynamic Saint-Venant equations.
The objective of HEC-RAS is to solve river dynamics and river engineering problems. Applications are delimitation of flood areas, embankment and protection work design, river restoration, emergency plans for dam break, optimization of hydraulic work and risk assessment and management.
This course develops the main functions and applications of the latest version of the MODFLOW 6 groundwater modeling code through the ModelMuse interface, both developed by the United States Geological Survey (USGS). This version includes innovative tools for the construction and simulation of hydrogeological models, mainly highlighting the incorporation of the discretization option for discretized by vertices grids.
The development of machine learning and geospatial libraries in Python as Scikit Image, Rasterio and Fiona gave us a new range of tools to analyze vegetation from traditional sources as satellite imagery and new sources as drone orthophotos. The appliance of Python programming for crop identification depends on many factors such as the image resolution, the vegetation stage and even the algorithm setup.
We have developed a course crop identification / delineation with geospatial and machine learning tools of Python, the course is aimed to GIS or related professionals that have basic Python knowledge. The course covers the introductory concepts of the geospatial libraries and a series of machine learning applications for crop indentification in olive trees, palms, agave and corn fields.
Note:
This course is for professionals with previous knowledge of Python. Basic concepts of Python programming will not be covered in the lessons and course material. In case you don’t have any knowledge of Python, we recommend you to take this online course.
The Hydrologic Modeling System (HEC-HMS) is a free program developed by the US Corps of Engineers that implements a series of hydrological methods to represent different physical processes of the water cycle.
HEC-HMS also includes procedures necessary for continuous simulation including evapotranspiration, infiltration, hydrological routing and soil moisture accounting. The tools and options of HEC HMS make it a very versatile and powerful software for the hydrological simulation of different scenarios such as extreme events in arid regions, or water balances in wet climates.
Hatarilabs offers the online course of Hydrological Modeling with HEC-HMS for students and professionals of any country. The course is focused on theory and practical to know the different HEC-HMS tools and its application to the hydrological modeling.
Hatarilabs offers the online course of Hydrological Modeling with QSWAT for students and professionals of any country. The course is focused on the acquisition and preprocessing of data for SWAT+ using QGIS 3.4 for develop hydrological watershed modeling.
This course covers the whole procedure of developing regional groundwater models through the ModelMuse interface, including the model construction, boundary conditions, parameter assignment, model simulation, results analysis and visualization, and model calibration. Flopy is also introduced in the course to import, create, refine and simulate the model as in ModelMuse. Two basins with different aquifer setting and boundary conditions are used as case studies to demonstrate the use of these tools so that the participants learn how to build these models and analyze the results for future decision-making.
Evaluation of long term impacts of the mining activity is a key topic on the public concern and regulatory and sustainability policies. Due to the scale of the mining projects and the different related activities, impacts could be made to several parts of the physical environment. Groundwater modeling is a useful tool for the simulation of mine closure impacts to the groundwater flow system and groundwater quality. Hatarilabs has developed an applied course about the simulation, calibration, representation and impact analysis of a waste dump closure on the environment and water bodies with a long term scope (closure and post closure). The course will be given on Model Muse for flow and contaminant transport modeling and scripts in Flopy for model calibration, water balance analysis and evaluation of remediation techniques performance.
This course is focused on how to use the different packages related to numerical tools, data analysis, representation of graphs, and others. Many of these tools are applicable to the field of Hydrology in the preparation of data for hydrological models, statistical analysis of hydrological parameters and model result representation.
Graphical user interfaces (GUIs) are commonly used to construct and post-process numerical groundwater flow. Flopy is the Python library that builds and executes MODFLOW models; this library has tools and options for the complete geo-referencing of a MODFLOW model.
Python is a simple and powerful programming language; its simplicity is remarkable compared to other programming languages and its power is based on the number of tools available for different areas of study.
Use of Python with the available scientific packages and FloPy, facilitates data exploration, alternative model evaluations and model analyses that can be difficult to perform with GUIs. One advantage of Flopy and Python is that there are many packages available to facilitate the model development process, including packages for plotting, array manipulation, optimization, and data analysis.
This course allows acquired knowledge of the open source programming language to construct model input files, run the model, and read and plot simulation results.
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With the upcoming release of mf6Voronoi v0.0.38, optimizing mesh generation workflows remains a top priority. Constructing complex unstructured Voronoi grids for MODFLOW 6 can become computationally intensive as spatial resolution increases. To address this challenge, parallel execution capabilities were introduced into mf6Voronoi leveraging dask-geopandas.
This tutorial explores a powerful interactive GeoPandas tool that facilitates the visual analysis and representation of water chemistry data. Through this Python workflow, you can display sampling points over satellite imagery and various basemaps, map concentrations using automatic color scales, and adjust marker sizes based on analyzed parameters. This direct integration allows you to customize the variables displayed on hover, streamlining data evaluation for baseline studies or water pollution diagnostics.
Interpolating large volumes of spatial data through the QGIS graphical interface is often a headache: the process takes too long or, even worse, the program freezes while consuming all system resources. In this tutorial, we will show you how to interpolate nearly 2 million points in less than two minutes using the OSGeo4W Shell and GDAL commands with the IDW method. By creating a virtual layer file (.vrt), you will learn a highly efficient, reliable, and lightweight workflow that enables you to work with millions of data points without being constrained by the limits of the GUI.
This tutorial demonstrates how to accurately and efficiently extract the water surface elevation at a specific point from a HEC-RAS 2D unsteady flow model using Python in Jupyter Lab. Since water levels vary across the mesh and native software tools can limit automation, the author teaches how to open HDF5 output files (.hdf), load a point shapefile using GeoPandas, and apply a geospatial index K-d tree to locate the nearest computational cell. Finally, the time series data is processed to generate a water level hydrograph, which is essential for hydrogeological model calibration.
estimations of crop water requirements. This applied case offers a deep dive into modeling crop evapotranspiration using the standardized FAO-56 Dual Crop Coefficient methodology, implemented programmatically through Python.
Using a real-world case study of olive cultivation (Olea europaea) in the hyper-arid coastal desert of Bella Unión (Arequipa, Peru), we will demonstrate how to build an end-to-end hydrological data pipeline. Participants will learn how to integrate spatial data, process satellite-derived and weather station climate parameters, define soil-water-crop constraints, and simulate complex deficit drip-irrigation schedules.
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FloPy is the Python library that builds and executes MODFLOW models; this library has been enhanced to provide full support of MODFLOW 6 with most of its recent development is related to functionality for MODFLOW 6, tools to use vector and raster spatial data and common plotting and export functionality.