ABSTRACT:

ChatGPT has forever changed the way that many industries operate. Much of the focus of Artificial Intelligence (AI) has been on their ability to generate text. However, it is likely that their ability to generate computer codes and scripts will also have a major impact. We demonstrate the use of ChatGPT to generate Python scripts to perform hydrological analyses and highlight the opportunities, limitations and risks that AI poses in the hydrological sciences.

Here, we provide four worked examples of the use of ChatGPT to generate scripts to conduct hydrological analyses. We also provide a full list of the libraries available to the ChatGPT Advanced Data Analysis plugin (only available in the paid version). These files relate to a manuscript that is to be submitted to Hydrological Processes. The authors of the manuscript are Dylan J. Irvine, Landon J.S. Halloran and Philip Brunner.

If you find these examples useful and/or use them, we would appreciate if you could cite the associated publication in Hydrological Processes. Details to be made available upon final publication.

ABSTRACT:

CMBEAR (the Chloride Mass Balance Estimator of Australian Recharge) is a Jupyter notebook that, as the name suggests, provides a simple and highly reproducible approach to estimate groundwater recharge using the Chloride Mass Balance method for Australian groundwater data. The notebook is set up to estimate recharge using Australian data and can be used in other regions if a gridded chloride deposition map is provided.

The notebook was written by Dylan Irvine (Charles Darwin University). The approach uses maps of Chloride deposition from Davies and Crosbie (2018, Journal of Hydrology), maps of long-term average rainfall (1916-2015) calculated from data from the Bureau of Meteorology, and user-supplied groundwater chloride concentrations (with associated latitude/longitude information) to apply the chloride mass balance method.

NOTE: The Jupyter notebook is associated with a methods note at Groundwater. If you use CMBEAR, could you please cite the Groundwater paper:
Irvine, D.J., Cartwright, I. (2022) CMBEAR: Python-Based Recharge Estimator Using the Chloride Mass Balance Method in Australia, Groundwater, 60 (3), 418-425, doi: https://doi.org/10.1111/gwat.13161.

The notebooks are simple to apply, with the main input being a simple spreadsheet.

The files contained here are:
- CMBEAR.zip, which contains all of the files required to run the tool.
- NT_data_prep.zip, which contains the data files to prepare an input file to estimate recharge in the Northern Territory of Australia
- Vic_WT_map_upload.zip, which contains a description of how input files were prepared to assess groundwater recharge using a gridded water table salinity map.

Enjoy
-Dylan Irvine

Version comments:
V1.01 - Minor fix to allow .csv as input
V1.0 - Original version