Python is a useful tool for data analysis but also for data representation and as a graphic tool. This is an applied tutorial for the representation of a wind rose with Python from wind speed and direction stored on an Excel spreadsheet. The tutorial explores the options of the library to represent windroses as bars, boxes, polygons or contours.

We present our own webapp for the representation of the Piper Diagram, Stiff Diagram and Scholler Diagram. The webapp was developed in Python Django and it is entirely free for everyone. The main objective behind this webapp was to develop a user friendly and minimum requirement tool to create these water quality / hidrogeochemical diagrams. The video tutorial shows the complete procedure to update the working file and then generate the diagrams.

A Piper Diagram is 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 calcium (Ca), magnesium (Mg) and sodium (Na). The most common anions are bicarbonate (HCO3), sulphate (SO4) and chloride (Cl).

The Piper diagram can be made by free and commercial desktop software, however in this tutorial we have generated the Python scripts and working procedure to create a Piper Diagram from values stored in a working spreadsheet.

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.

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).

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:

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).

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.

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.

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. 

Discretization is the “art” of transforming a continuous media as nature into discrete parts; for numerical models the spatial and temporal discretization have become a key issue in assuring model efficiency, output precision and the overall quality of the modeling work. Flow models are constructed to represent an specific requirement on the surface water/ groundwater flow regime (local scale), however, the model has to represent first the overall flow regimen (global scale). On the general model areas, an efficient spatial discretization criteria rules to keep the mesh elements as big as possible, meanwhile, in the areas of interest the model should be discretized into the smallest parts.