Live Course: Python for Hydrology and Hydrogeology
Explore Python programming for water modelling.
In this 3-session course attendees will learn how to perform; data wrangling and multivariate exploratory data analysis, time series analysis and data visualisation. Each session is hosted by experts in their field, who will delve in-depth into the key topic, explaining best practice techniques and approaches. Practical examples and demonstrations will be analysed, allowing attendees to apply the knowledge gained and learn hands-on how to use Python- in order to save you time, money and integrate systems effectively.
The course consists of 3 live and interactive 2-hour sessions, over three weeks. There is also pre and post course material to develop and solidify your learning. Don't miss the opportunity to have your questions answered via our interactive learning platform.
Date:
Thursday, 3 June 2021 - Thursday, 17 June 2021
Luk has over 10 years research experience in environmental impact assessment and modelling groundwater dynamics at regional to continental scales for water resource management. His research features a... Read more
Chris is an Adelaide-based hydrogeologist in Land and Water's Regional Scale Groundwater Analysis team. His research primarily involves the characterisation of various aspects of regional-scale ground... Read more
Vincent is a hydrogeologist with over 10 years of experience in Python programming. He uses it on a daily basis for many if not all of his tasks, such as working with logger data, preparation of model... Read more
LIVE COURSE FULL. SIGNUP FOR ON-DEMAND COURSE to receive the recordings on the 24th June.
This course is designed to be highly practical, with 6+ hours of training session recordings.
Course Contents
The course will cover 3 main topics across 3 sessions.
Session 1 | Lead by Luk Peeters
Data wrangling and multivariate exploratory data analysis
1. Hydrochemistry data
Download data from GA’s website
Explore data through scatter plots
Make a Piper plot (and map)
Multivariate analysis: principal component analysis, clustering (touching on machine learning in scikit learn)
2. Exploratory Data Analysis APY Lands groundwater dataset
Load pre-compiled dataset
Summarise dataset
Visually explore relationships and test hypothesis in the dataset using violin-plots
3. Datacube: AWRA
Load dataset of Australian Water Resources Assessment model from Bureau of Meteorology website
Visualise and explore maps and time series of AWRA outputs
Multivariate analysis and visualisation
Session 2 | Lead by Chris Turnadge
Time series analysis
1. Data pre-processing
Using interpolation to fill gaps
Detection and removal of outliers
Resampling to higher or lower sampling resolution
Temporal differencing
Detrending data using time and frequency domain methods
2. Decomposing hydrograph data
Quantifying the relative contributions of component processes
3. Interpreting responses to time-lagged processes
Demonstration of convolution
Regression deconvolution
4. Interpreting responses to periodic processes
The discrete Fourier transform
Periodogram-based approaches
Harmonic least squares
Session 3 | Lead by Vincent Post
Data visualisation
1. Data visualisation and linking it with Google Earth
2. Visualisation of modelled flowpaths in 3D
3. Evaluation of pumping tests
Format
The course is delivered through units via the learning platform
6+ hours of session recordings
Pre-and-post-course materials to go through via the learning platform.
Exercises between the live sessions.
Manual of the course and working model download/s.
Ability to access all the online course materials for up to a month after the course. The pre-readings/videos and manual/s are available for ongoing learning.
Preparation
Pre-course reading and video watching is encouraged.
Requirements
A good internet connection and software and downloads as described in the learning platform.
Outcome
On completion of the course attendees will be issued with a Certificate of Participation.
