There are three essential things for the representation of geological data on QGIS: the spatial geological information, an appropriate symbology and the software knowledge. Once these three things are available, the potential of QGIS to represent geological maps is unlimited.
We have done a tutorial for the representation of synclines, anticlines, overturned synclines and overturned anticlines on the regional scale with QGIS.
Read MoreIn 2017, the U.S. Geological Survey released a new core version of the MODFLOW program. This new version, called MODFLOW 6 (the sixth core version), extends the core MODFLOW capabilities to include robust solutions for complex water table problems, support for generalized meshes with focused resolution within areas of interest, and support for multiple models and multiple types of models within the same simulation. In addition to the Groundwater Flow Model, MODFLOW 6 now contains a Groundwater Transport Model, which can run simultaneously with the flow model or as a separate simulation using the results from a previous groundwater flow simulation.
Read MoreThe speciation modeling allows to calculate the distribution of aqueous species in a solution. Phreeqc is capable to simulate this speciation calculation and we are going to demonstrate this capability on a study case of aqueous species in seawater.
We have done a tutorial for the speciation modeling of seawater with Phreeqc that runs under Python in a Jupyter Lab enviroment. The code can run the Phreeqc executable, define the databases and stablish the output files. Results from simulation are available as Pandas dataframes and plots are made for the main components and the distribution of saturation indices.
Read MoreWe have done a tutorial that covers the simulation process and output visualization from a basin scale hydrological model in SWAT+. The tutorial imports an existing QSWAT+ project, reviews the main geospatial parts of the model, runs the SWATPlusEditor and imports model results into the database. Finally, the available options for output data visualization in QGIS are explored for HRUs, channels and the whole basin.
Read MoreThe process of retrieving information from satellite imagery could be time consuming and faces challenges like image resolution, feature recognition and user criteria. With the use of machine learning tools we can preprocess images and combine them with standard QGIS tools to delineate objects in a much efficient way.
We have done a tutorial for the delimitation of water bodies as lakes from a panchromatic Landsat 8 image. The tutorial shows a mixed procedure that detects edges with Python and Scikit Image , traces paths with QGIS and the Trace Raster plugin and finally gets the lake extension as a Shapefile.
Read MoreA good spatial representation of the geological features requires not only high quality field data but also the right symbology and graphical resources. We can define QGIS as the tool that can create awesome geologic maps but still some resources have to be developed for a massive use. This tutorial shows the procedure to represent normal and inverse faults with symbology included on the input data files.
Read MoreLatest developments in QGIS and online services allows us to create amazing maps in less time. Different types of layers are available XYZ tiles as terrain, satellite, road maps. This time we show the procedure to add 2 types of topograpy layers in QGIS 3 behind geological features.
Read MoreDrone imagery shows us features on the surface with high precision and machine learning tools allows us to understand and get information from those images. We present a tutorial in Python together with Scikit Learn and geospatial libraries that delineates crop rows on a corn field and provides results as a vector spatial file.
Read MoreHydrological process of precipitation - runoff can happen in small areas however the common tools to evaluate the drainage networks are mostly related to the regional scale. Lidar devices provide millions of 3D points with resolution capable of defining drainage basins on much larger scales and delineate channel networks of neighborhoods or city districts.
We have done a tutorial for the channel network and drainage basins delineation over an urban area in Los Angeles, USA. The tutorial shows the whole procedure from downloading the Lidar data, processing the Las files and processing the rasters with QGIS.
Read MoreThe topic of open source software and environmental assessments has some interesting associations. For example, if an investment project delivers a groundwater model in Xflow software, the environmental evaluator needs an Xflow license in order to open the model, review the simulations, and report on their observations. But what about civil society? or the communities that have an interest in knowing how the model is built or in the results of the predictive simulations, do these people also need an Xflow license? And what happens if another regulator wants to review the model, can they do so or they can only ask for a technical opinion?
Read MoreIn our research for new geospatial tools in Python and better ways to deal with geospatial data we found that complex or full featured processes are already included in the common spatial libraries as Geopandas. We have developed an applied example to upload point / line / polygon ESRI Shapefile to a Postgres / Postgis database with Python, Geopandas and SQL Alchemy in a few lines of code. Besides, we have simplified the procedure to run a Postgres database inside the Hakuchik Docker image fully connected to QGIS.
Read MoreIn our perspective, 3D visualization of geospatial data has been a long desired feature that has been covered in some features from SAGA GIS or in some plugins from QGIS. This time we developed a Python script that converts point / line / polygon ESRI shapefiles (or any vector file) to unstructured grid Vtk format type (Vtu) by the use of the Python libraries Geopandas and Pyvista. The tutorial has files, scripts, and videos that show the whole procedure with some remarks on the software and spatial files and a discussion about the nature of the spatial files that presents some challenges in the data conversion.
Read MoreAs a peruvian slang the word “chicha” means mixture, but a mixture of a high level of heterogeneity, something like doing a pizza with all the ingredients from your refrigerator. This time we have developed a tutorial with this “chicha” spirit since it combines a database, sql language, a virtualization engine, Python, a variant of SQL Alchemy, port forwarding and connection to QGIS in Windows. Certainly, we are not code developers by nature, but we are pretty happy to have achieved this level of complexity or mixture.
We have developed an applied tutorial for the implementation of a Postgresql database with Postgis enabled on a Docker image. Information about groundwater well locations have been inserted from a CSV file with Python and Geoalchemy and Docker ports have been forwarded and open to be accessible from QGIS in Windows.
Read MoreQGIS has a wide range of options for the representation of vector data (point, line and polygon) but specific workflows have to be established to apply QGIS in different fields such as Geology. We found that there were few resources about how to plot geological features as bedding planes, faults, synclines and other and we have decided to do a series of tutorials showing those procedures on applied examples.
This time, we have developed a tutorial with the complete procedure to plot the azimuth and dip of bedding planes with QGIS. The tutorial imports a SVG marker that rotates according to a field value while the dip is presented as a label.
Read MoreHowever, based on what we see we still have some doubts about the way that we are learning software in water resources, and we are concerned if the level / rate of knowledge shared will cope with the next challenges of humanity as climate change in water resources meaning droughts, floods and others or developing sustainable policies in water resources.
Read MoreDealing with spatial data in real life implies dealing with styles for vector data and unfortunately, there we find a huge gap between commercial and open source software. There are some tool to overcome these problems as the QGIS plugin Slyr.
This time we have done a tutorial with whole procedure to change the Esri style (.style files) for geological units to a Xml file with Slyr that is applied to the polygon style in QGIS.
Read MoreThe delineation of watersheds and stream networks is one of the most fundamental steps on water resources analysis. Working with QGIS for water resources shows some challenges on the normal water resources workflow since we have to couple raster / vector datasets, coordinate system, and algorithm limitations.
We have developed a tutorial with the whole procedure to create the watershed and stream network on vector format for large scale basins in QGIS 3. Steps range from elevation datasets download for Nasa Earthdata, creating virtual rasters, lower resolution, change coordinate system, fill sinks, create channel networks, calculate upslope area, polygonize rasters and others.
Read MoreRepresentation and analysis of flow and transport results is a challenge for groundwater modelers. Aspects such as speed, compatibility, data format and visualization options are key in the 2D/3D representation of head and concentration on model cells. Under the Jupyterlab framework new tools have been developed that can be useful to represent model results on a user friendly way .
This time we have done a tutorial for the interactive representation of a contaminant plume in Jupyterlab; modeling was done with Modflow Nwt and Mt3d, representation was done with Matplolib, Ipywidgets and Voila.
Read MoreWhether you want to explore or visualize a Shapefile without leaving the Jupyterlab environment or you just want a better tool for the spatial representation this tutorial might be of your interest. We have developed an interactive application on Jupyterlab with Ipyleaflet and Voila that represent a selected shapefile on a canvas with options for basemaps, zoom and others.
Read MoreOn the way to discover the analogy of the common desktop GIS procedures to Python we assumed that the process of extracting contours from raster was well documented or there were many tutorials on the topic. We found that there weren't many options to complete this process successfully or with few amount of pain, therefore we have done a complete tutorial on the process to create contours from a elevation raster with Python and GDAL that includes input data, scripts, and a discussion over the main steps of processing.
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