On 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.
Read MoreWhen 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.
Read MoreGeopandas 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.
Read MoreOn 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.
Read MoreIn 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.
Read MoreOpen 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.
Read MoreFor 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.
Read MoreEducation 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.
Read MoreDeveloping 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.
Read MoreEvaluation 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.
Read MoreUntil now we had limited options to provide specific support in groundwater modeling. We know that modeling requires the review of extensive documentation and practical work with the software and sometimes or most times we don't have the time to sort things out.
Read MoreWe 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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