Interactive visualization of aquifer response to pumping with MODFLOW6, Flopy and Jupyter

Aquifer response to pumping is one of the most popular interactions between human and the groundwater flow regimen. On the complexity of the hydrogeological studies, pumping tests are the most controlled environments since the well construction details are known, geological logs are available and pumping rates and drawndows can be measured. There are uncertainties on this hydraulic test and these are mostly related to the aquifer heterogeneity, however, it is expected that a pumping test can be fully recreated on a numerical model.

MODFLOW is the groundwater flow model developed by the USGS based on finit

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Flood Monitoring Tutorial with Sentinel 2 and Landsat 8 Images in QGIS 3

With current technology and the availability of remote sensing tools through different servers makes it possible to determine or estimate the areas that are flooded or could be, the focus of this tutorial in which a methodology to determine flood zones will be described from the calculation of the NDVI and compare the results with the use of two servers, Sentinel 2 and Landsat 8.

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Vegetation and Biomass Indices Estimation with QGIS 3 - Tutorial

The vegetation indices are obtained from area and satellite images and can be used to estimate changes in the state of vegetation, biomass, leaf area index and chlorophyll concentration. The determination of vegetation indices is calculated from the relationship between the reflectance of the electromagnetic spectrum. While biomass presents various methodologies to be able to estimate that they are based on field measurements that despite being a direct method are still very limited. Currently, the use of remote sensors provides a method to generate information on biomass.

In this tutorial the estimation and relationship of the biomass of a study area with the Normalized Difference Vegetation Index (NDVI) and the Normalized Red-Green Difference Index (NGRDI) will be carried out.

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Determination of mine polluted lakes with QGIS 3 and SCP 6 - Tutorial

Mining and the treatment of metals are a significant source for the contribution of pollutants in various natural resources such as lakes. The impact of a pollutant on the lakes depends on the physical and chemical conditions that affect its toxicity and the degree of exposure related to concentration and time. The contribution of metals can be incorporated by precipitation, mine filtration, soil washing, leaching and percolation.

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Spectral signature behavior analysis for seawater quality with QGIS 3 and SCP plugin - Tutorial

Remote sensing allows the interpretation of dynamic processes in a study area at a spatial and temporal level. This information is collected from the surface that is captured by a sensor located on board a satellite in orbit around the earth. In this way, you can compare images and analyze the changes of a study area.

Different space agencies have facilitated access to information provided by satellites through servers and web portals, where satellite images can be downloaded for the analysis and interpretation of resources, for this instance, the quality of seawater can be analyzed through the classification of specific footprints using geographic information systems tools such as QGIS 3 and the Semi-Automatic Classification plugin.

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Basic example of groundwater modeling in MODFLOW 6 with Python - Flopy

MODFLOW 6 is the last version of MODFLOW that brings a set of new tools and a complete rearrange of the model file system. To the date of this post (July 2018) there are limited options for MODFLOW 6 preprocessors and postprocessors; so, whether you construct the MODFLOW 6 files as text files or you use the Flopy options to build, run and visualize groundwater models in MODFLOW 6.

Flopy is the Python package to create, run and post-process MODFLOW models. Flopy supports MODFLOW-2005 and MODFLOW 6 modeling codes and MODFLOW-based models as MODPATH (for particle tracking) and MT3D-USGS (for contaminant transport).

This tutorial show the complete procedure to setup, run and visualize a basic groundwater model in MODFLOW 6 with Flopy.

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Extracting geological faults and orientation representation in QGIS with GeoTrace - Tutorial

Image from earth surface are available from satellite images and unmanned aerial vehicules (UAV). Image resolution ranges from 10 meters on free available datasets as Sentinel2, less than 1 meter on commercial satellite imagery to 0.05m (or even less) from UAVs.

Scikit-image is a library for image processing in Python. Currently there are few QGIS plugins that run Scikit-image, we found one that extracts geological features as fractures and represent fault dip, dip direction, and strike in stereonets and rose diagrams. This tutorial show the procedure to install GeoTrace on QGIS 3 with its dependencies, runs a geological fault trace on a modified raster, calculate fault strike azimuths as a separate attribute field represent them as a strike rose.

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How to open HDF files on Sinusoidal Coordinate System in QGIS3 with PyQGIS - Tutorial

For a normal GIS user, QGIS 3 brings a lot of new tools, new forms to perform spatial analysis but also it doesn´t bring (yet) some options available in QGIS 2. This is the case for the representation of HDF raster files that is not available in QGIS 3 but it is available in QGIS 2. Research have been performed to address this issue and many options were evaluated to open the HDF files and perform a geotransformation from Sinusoidal Coordinate System to Geographical Coordinate System.

The solution came from the powerful gdal library and some core Python functions. This tutorial show the procedure to open a layer of a MOD13A2, a MODIS data product for vegetation evaluation and reproject it to geographical coordinate system (lat/lon).

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Land cover spectral signatures determination with QGIS 3 and Semi-Automatic Classification Plugin (SCP 6) - Tutorial

There are several satellites with different characteristics that acquire multispectral images of the surface of the earth. In this case, the Sentinel 2 images are particularly useful for the monitoring of land cover and can be provided free of charge from SCP.

In this tutorial we will perform the evaluation of spectral signatures using the Semi-automatic Classification complement in version 6, which is a free open source plugin for QGIS 3 that allows the supervised and unsupervised classification of remote sensing images.

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How to visualize water quality data in QGIS 3? From points to raster to contour lines - Tutorial

Spatial interpolation techniques used to evaluate estimations of physical and chemical constituents in areas where they are not estimated (Murphy et al., 2009). This tutorial will show how to interpolate data from point data to obtain a raster that covers the study area and, then, to obtain contour lines from the raster.

The Inverse Distance Weighted (IDW) method is one of the most used due to its simplicity. IDW expect that each point has a nearby impact that reduces with distance. It gives more weight to the points nearest to the forecast area. QGIS has the ability to perform this interpolation method by using data points and the result is displayed as a raster file.

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How to calculate the Root Mean Square Error (RMSE) of an interpolated pH raster?

The root mean square error (RMSE) has been used as a standard statistical parameter to measure model performance in several natural sciences. The parameter indicates the standard deviation of the residuals or how far the points are from the regression or modelled line. The following figure shows the residuals as green arrows and its location between the point data and the regression line. 

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How to create a boxplot to represent basin scale water constituents using Python - Tutorial

Python is an interpreted high-level programming language which allows performing several statistical procedures. This programming language is an excellent option to create box plots because of its simplicity and exceptional results. This tutorial explains how to download and use Python´s Jupyter Notebook to analyze water quality data in the form of boxplots.

Box plots show the distribution of a sample using the lower quartile (Q1), the median (m or Q2) and the upper quartile (Q3)--and the interquartile range (IQR = Q3-Q1), which covers the central 50% of the data. Quartiles are values that divide the data in quarters; the term refers to the value that falls in the line that divides each quarter. Therefore, Q1 is the highest value of the first 25% of the data, Q2 is the one of the 50% of the data and Q3, the one for the 75% of the data. Characterizing the data with quartiles is advantageous because they are insensitive to outliers and preserve information about the center and spread (Krzywinski & Altman 2014).

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What is a Piper diagram for water chemistry analysis and how to create one?

In 1994, Arthur M. Piper, proposed an effective graphic procedure to segregate relevant analytical data to understand the sources of the dissolved constituents in water. This procedure was born under the statement that most natural waters contain cations and anions in chemical equilibrium. It is assumed that the most abundant cations are two “alkaline earths” calcium (Ca) and magnesium (Mg) and one “Alkali” sodium (Na). The most common anions are one “weak acid” bicarbonate (HCO3) and two “strong acids” sulphate (SO4) and chloride (Cl). Less common anion and cation-constituents are summed with the major three anions and cations as shown in the following table:

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Dynamic Simulation of Hillslope Landslide with openFoam - Tutorial

This tutorial shows the whole procedure to simulate a landslide of a hillslope from a initial condition of failure. The tutorial was done with the interFoam solver from openFoam on a non- Newtonian flow. The fluid has a variable kinematic viscosity (nu) based on the Bird-Carreau model. Failure scenario last only 6 seconds and results were recorded every 0.1 seconds. Final geometry and the landslide development were analyzed with paraView with predefined views (paraView states).

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Modeling and Analysis between 5 Newtonian Fluids in OpenFOAM - Tutorial

Computational fluid dynamics modeling with OpenFOAM could be challenging for water resources engineers since OpenFOAM models all types of fluids like water, air, heat and electromagnetism. On a normal hydrological software, it is implicit that the physical properties or the empiric formulation matches water on the liquid phase at temperatures around 20°C; however in a CDF program as OpenFOAM we have to define that the fluid we are working with is water and this increases the level of complexity on the model conceptualization and analysis.

But there is a interesting face of this complex fluid formulation in OpenFOAM: we can model any fluid, fluid type and turbulence condition; that means that we can model fluids like oil, alcohol, beer, or glycerine just with their property definition. In this tutorial we model and compare the behavior of 5 Newtonian fluids: beer, benzene, glycerine, olive oil and water. All fluids have been simulated on the same geometry and timeframe and all simulation output have been integrated in one paraView session for the comparative analysis of the fluid performance. Fluids were modeled with the interFOAM solver on turbulent conditions with the k-epsilon schema.

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Floating Object Stability Modeling with OpenFOAM - Tutorial

This tutorial is about a floating object stability simulation from a water surface oscillation (wave). The model was done with the interFoam solver that is a solver for two incompressible fluids, on isotermic conditions using a volume of control (VOF) phase-fraction interface approach. Turbulence was conceptualized on the model with the kEpsilon turbulence model. Simulation was done for 4 seconds with outputs every 0.05 seconds and runs in almost 5 minutes on OpenFOAM for Windows, better computation times are expected when run on Linux with paralleling computing.

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Electric Conductivity Analysis of a River Pathway using QGIS 3 and the Profile Tool Plugin

Electrical Conductivity (EC) has been used to analyze the content of dissolved salts in water. EC refers to the ability to transmit electricity (Ikeda et al. 1991). Pure water has low values of EC because the electricity is conducted by the ions in solution; therefore, the greater the concentration of ions in water, the greater the value of EC. This tutorial explains how to analyze the EC of a river using the Profile Tool plugin in QGIS 3.0.

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How to visualize time series in QGIS 3.0 with the Time Manager Plugin - Tutorial

Time series in hydrology can be analyzed to a) detect a trend due to another random hydrologic variable, b) develop and calibrate a model, c) predict future characteristics of a variable (Machiwal & Jha 2012). The application of time series analysis is diverse; for instance, it can be used to evaluate global trends of soil moisture (Dorigo et al. 2012), to analyze river discharges (Papa et al. 2012), to detect glacial lake outburst floods (Veh et al. 2018) or to detect rainfall patterns (Wang et al. 2016).

The visualization of the data variability over the time can be a useful tool to identify patterns or to compare the behavior of different samples. The use of software for Geographic Information Systems (GIS) allows to identify the location of the samples and to compile the information that the samples have. Open-source software like QGIS offers excellent tools to achieve this objective. This tutorial will explain how to use the Time Manager Plugin.

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Modeling Effluent Disposal Mixing Zone into the Ocean with OpenFOAM - Tutorial

A right assessment of the effluent mixing zone would require a baseline of sea currents, discharge flows, seawater and effluent density, bathymetry, waves, infraestructure geometry and a tool that can analyse the interaction of the mentioned factors. OpenFOAM is a numerical model for Computational Fluid Dynamics (CFD) modeling capable of modeling fluids with complex geometries, conditions and requirements; with OpenFOAM one can model compressible/uncompressible, single phase / multiphase, flows that mix, non-newtonian flows, etc. OpenFOAM comes with build-in tools for model construction and visualization, and there is Salome Platform for advanced mesh generation.

This tutorial show the entire procedure for the simulation of a effluent of 40 l/s into the ocean that has a current of 0.05 m/s. The model is on transient conditions, model simulation were done under uniform discharge rates, the development of the mixing zone was analyzed with paraView tools and a water chemistry component was introduced into the simulation with some Python scripts. 

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Comparison of IMERG Precipitation with Station Information with QGIS, Python and Pandas - Tutorial

There are tools for temporal data analysis like Python, IPython and Jupyter; there are tools for spatial data analysis like QGIS. But, are there tools for spatio-temporal analysis? Unfortunately no, but there are good approaches to manage spatial data in Jupyter or to run IPython in QGIS3. These approaches aren't a complete ansqwe to the current demands of big data processing in few computational time with simple scripts, but by sure it will help to shape better solutions.

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