Smart analysis of online temperature sensor data with SensorThingsAPI and Python - Tutorial

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This tutorial demonstrates how to connect to a SensorThings API FROST server using Python to retrieve, inspect, and visualize IoT sensor datastreams. After setting up the server connection and querying datastream details such as units and observation counts, the notebook extracts time-series observations using paginated REST requests and loads them into a Pandas DataFrame. Following the data preparation phase, the script produces a series of continuous time-series line charts using Matplotlib and Seaborn, displaying the chronological trend of air temperature measurements recorded over time.

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Introduction to Online Sensor Analysis with SensorThings API and Python - Tutorial

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In this Hatarilabs tutorial, viewers learn how to access and query live environmental sensor data using Python and the OGC SensorThings API standard. The video walks through setting up a Jupyter Notebook environment to interact with a FROST-Server, explaining key SensorThings API concepts—including Things, Sensors, Observed Properties, and DataStreams. Using packages such as requests, pandas, and datetime, the session demonstrates how to apply API parameters like $top, $skip, $orderby, and $expand to efficiently filter and retrieve time-series observations, culminating in a data frame visualization of temperature readings from a live outdoor sensor.

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Benchmarking mf6Voronoi v0.0.38: Parallel Performance, Scaling Limits, and Mesh Refinement Dynamics

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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.

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How to Interpolate Over 2 Million Points Quickly and Without Freezing - Tutorial

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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.

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Plotting Water Surface Elevation from a 2D HEC-RAS Model using Python - Tutorial

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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.

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Advanced Crop Water Demand Modeling: Simulating ETo, ETc, and ETa with Python and pyfao56 - Tutorial

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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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7 tutorials for coupling geological information into groundwater flow models build with Model Muse

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Next challenges on groundwater modeling facing scenarios of climate change and sustainable groundwater management require the implementation of fully coupled geological models into groundwater flow models. Even though we have dealt with this topic, we still have a lot to do in providing applied cases / tutorials for the hydrogeological unit conceptualization based on geological information with open source codes such as MODFLOW.

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Determination of the Depression Cone from an Open Pit with MODFLOW6, Flopy and mf6Voronoi - Tutorial

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One of the key objectives from groundwater modeling in mining is the determination of pit inflows and the extension of the depression cone. This tutorial shows an applied case of determination of a depression cone from an open pit model built with MODFLOW6, Flopy and mf6Voronoi. The model has faults and the tutorial covers all steps from model import, data processing and export of geospatial data from the depression cone.

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Geospatial Crop Recognition from Drone Orthophotos with Python and HatariTools - Tutorial

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Spatial analysis and machine learning sometimes require massive coding in order to achieve decent results such as identifying plants from a drone orthophoto. We wanted to create a simple workflow for beginner and intermediate Python users to work with these libraries without much pain or frustration. This tutorial has the complete procedure to use a new Python package called hatariTools that recognizes plants from an orthophoto based on sample points and creates intermediate plots and identifies plants as point shapefiles.

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Open, modify, run, and read output from HEC-RAS models with Python - Tutorial

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Python can communicate with other softwares through standards in Windows (COMs) and thus provides a higher level of interoperability for river flow models made in HEC-RAS. The amount of tools provided by HEC-RAS through the COM is huge; in this tutorial we will cover some examples of HEC-RAS model interaction for two applied cases from Python scripts in Jupyter Lab.

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How to create a Geological Model from a Point Shapefile with Gempy and Aquifer App - Tutorial

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This is an applied case were we build a geological model only with lithology information stored as a point shapefile. The tutorial covers all steps from raster (array) generation for all surfaces together with the orientation sampling and format of surfaces/orientations as Gempy input files. The generated data was inserted in the Aquifer App that implements an interface to create Gempy models. Finally the lithology and layer surface geometry was exported as Vtk to be represented on Paraview with the initial data to evaluate the accuracy of the simulation.

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NetCDF for water resources with Python for dummies (CHIRPS dataset) - Tutorial

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NetCDF has become a popular choice for storing and delivering precipitation and water resources related data. Its capacities to store multiple geospatial raster layers over time allow another level of abstraction on data analysis, however the format is of limited use on normal desktop applications and most times we are required to use a programming language such as Python or R.

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Introduction to Python and Geopandas for Flooded Area Analysis - Tutorial

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Geopandas is one of the most advanced geospatial libraries in Python because it combines the spatial tools of Shapely, it can create and read different OGC vector spatial data, it can couple the Pandas tools to manage, filter, and make operations over the columns of the metadata, it has the capability to plot geospatial data on Matplotlib and even to Folium among other features. We have developed a tutorial of Geopandas applied to the analysis of flooded areas over the Boise city for a return period of 200 years; the tutorial covers introductory concepts of Geopandas, it will work with point, line and polygon vector data, create plots, simplify vertices and perform geospatial queries over inundated facilities and highways.

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Modeling of mixing water / effluent with OpenFoam and HatariUtils - Tutorial

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Modeling flow on open channels and simulating water mixing to evaluate the final concentrations and mixing area was a hard task with conventional open source software. Based on our research and app development we came up with a process to model flow of water and effluent mixing and then calculate the mass flux with the initial concentration of certain chemical components to have a fully 3d simulation of the concentration development with time and distance from source.

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Are hydrogeologists / numerical modelers forbidden from cloud services?

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In 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.

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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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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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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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