2016 Narrabeen Storm EWS Performance

This repository investigates whether the storm impacts (i.e. Sallenger, 2000) of the June 2016 Narrabeen Storm could have been forecasted in advance.

Repository and analysis format

This repository follows the Cookiecutter Data Science structure where possible. The analysis is done in python (look at the /src/ folder) with some interactive, exploratory notebooks located at /notebooks.

Development is conducted using a [gitflow](https://www.atlassian .com/git/tutorials/comparing-workflows/gitflow-workflow) approach - mainly the master branch stores the official release history and the develop branch serves as an integration branch for features. Other hotfix and feature branches should be created and merged as necessary.

Where to start?

Check .env Uses pipenv

1. Clone this repository.
2. Pull data from WRL coastal J drive with make pull-data
3. Check out jupyter notebook ./notebooks/01_exploration.ipynb which has an example of how to import the data and some interactive widgets.

Requirements

The following requirements are needed to run various bits:

• Python 3.6+: Used for processing and analysing data. Jupyter notebooks are used for exploratory analyis and communication.
• QGIS: Used for looking at raw LIDAR pre/post storm surveys and extracting dune crests/toes
• rclone: Data is not tracked by this repository, but is backed up to a remote Chris Leaman working directory located on the WRL coastal drive. Rclone is used to sync local and remote copies. Ensure rclone.exe is located on your PATH environment.
• gnuMake: A list of commands for processing data is provided in the ./Makefile. Use gnuMake to launch these commands. Ensure make.exe is located on your PATH environment.

Available data

Raw, interim and processed data used in this analysis is kept in the /data/ folder. Data is not tracked in the repository due to size constraints, but stored locally. A mirror is kept of the coastal folder J drive which you can use to push/pull to, using rclone. In order to get the data, run make pull-data.

List of data:

• /data/raw/processed_shorelines: This data was recieved from Tom Beuzen in October 2018. It consists of pre/post storm profiles at every 100 m sections along beaches ranging from Dee Why to Nambucca . Profiles are based on raw aerial LIDAR and were processed by Mitch Harley. Tides and waves (10 m contour and reverse shoaled deepwater) for each individual 100 m section is also provided.
• /data/raw/raw_lidar: This is the raw pre/post storm aerial LIDAR which was taken for the June 2016 storm. .las files are the raw files which have been processed into .tiff files using PDAL. Note that these files have not been corrected for systematic errors, so actual elevations should be taken from the processed_shorelines folder. Obtained November 2018 from Mitch Harley from the black external HDD labeled "UNSW LIDAR".
• /data/raw/profile_features: Dune toe and crest locations based on prestorm LIDAR. Refer to /notebooks/qgis.qgz as this shows how they were manually extracted. Note that the shapefiles only show the location (lat/lon) of the dune crest and toe. For actual elevations, these locations need to related to the processed shorelines.

Notebooks

• /notebooks/01_exploration.ipynb: Shows how to import processed shorelines, waves and tides. An interactive widget plots the location and cross sections.
• /notebooks/qgis.qgz: A QGIS file which is used to explore the aerial LIDAR data in /data/raw/raw_lidar. By examining the pre-strom lidar, dune crest and dune toe lines are manually extracted. These are stored in the /data/profile_features/.

TODO

• Setup precomit hook for automatic code formatting using black.
• Raw tide WL's are interpolated based on location from tide gauges. This probably isn't the most accurate method, but should have a small effect since surge elevation was low during this event. Need to assess the effect of this method.
• Estimate max TWL from elevation where pre storm and post storm profiles are the same.
• Mitch updated the raw profiles.mat to include more information about the survey time. Our data scripts should be updated to parse this new information and include it in our dataframes.
• Implement (bayesian change detection algorithm)[https://github.com/hildensia/bayesian_changepoint_detection] to help detect dune crests and toes from profiles