Will the future of Water Resources Software run a Jupyter Notebook?

We are sure that in the future most of water resources software will be open source, but somehow there are doubts about the operating system, the platform and some requirements of these future software. It might be that concepts of this article are against the mainstream on the development of water resources evaluations, but we are talking about the future, a interesting stage in our concern for the sustainable management of water resources, and a stage with lots of uncertainties, challenges and trends.

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Implementation issues of Open Source Software in Mining Water Management

A year ago I had the opportunity to talk about open source software for water resources to several mining professionals. I remenber that I gave the maximum of my communicational skills to describe the complete perpective of the available open source tools for water resources as well as specific applications on the mining industry. Based on my experience as teacher, along the conference development, I knew that there was a problem on the communication process; the language was right, the channel was okay, but it might be that the message was wrong. At the end of the presentation, there was no questions, not even a single commentary about an application of open source software in mining water management.

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Overview of QuickMapServices Plugin for Basemaps in QGIS3 - Tutorial

QGIS 3 has a lot of new features and new tools for geoprocessing and spatial analysis, however there are some tools from QGIS 2 that hasn´t migrated yet. An example of this break was the OpenLayers plugin, one of the most popular and first plugins in QGIS 2 that is not available in the new version of QGIS.

OpenLayers plugin provide maps from Google, OSM and Bing in QGIS 2. Basemaps in QGIS 3 can be inserted as a XYZ Tiles on a process described on this tutorial. However, QuickMapServices plugin allow the search and implementation from a set of basemaps on different formats (TMS, WMS, WFS and GeoJSON) in QGIS 3.

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Two-dimensional transport modeling in a radial flow field with MODFLOW and MT3DMS

MT3DMS, a three-dimensional transport model, will be used in this tutorial to simulate two-dimensional transport in a radial flow field. The example consists on a well which is injecting a solution in a constant rate of 100 m3/d with a contaminant concentration of 10 g/m3 (10 mg/l). This model will run for a total of 27 days.

The following tutorial will explain how the model was build and the conditions considered.

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Tutorial for the installation of Freewat: the MODFLOW interface in QGIS

MODFLOW itself do take into account the spatial referenciation of the groundwater flow regime. Main MODFLOW output are water heads on the cell centre and flow in between cells, additional packages calculate solute transport, zone budgets or pathlines; however on the model construction and simulation it doesnt matter where the model is.

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Dynamic Flood Simulation of Combined Peak Flows with HEC-RAS - Tutorial

Flood events come from periods of high precipitiation and favourable soil moisture conditions. Based on the basin topography, land cover and precipitation distribution, flood events can be conceptualized as the cummulative sum of a series of peak flows from different affluent rivers.

Flood management involve the prediction of river water elevation and velocitiy from extreme precipitation events. This tutorial shows the procedure to build a unsteady (dynamic) flow simulation of two peak flows with different hydrograms. The channel network configuration for the area of study consists of one main river and a affluent river. Peak flow in the main river is 10 hours ahead of the peak flow in the affluent river.

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Basic Example of QGIS - HECRAS Geometry Construction and Flood Simulation

Flow simulation is an important task for engineering and disaster management. From the free softwares available for flow simulation, HEC-RAS is a strong alternative for its continuous development, the capability to simulate many flow types, and the interaction with other softwares like QGIS or MODFLOW.

QGIS is a free and open source software Geographical Information System (GIS) application. There is a environment of available plugins in QGIS that perform specific tasks of analysis, representation and preprocessing. There are QGIS plugins related to HEC-RAS, in this tutorial we will use the Q-RAS plugin to develop a basic example of flow model geometry construction in QGIS and steady flow simulation in HEC-RAS.

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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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