ABSTRACT:

===== General Overview ====
These datasets correspond to the manuscript "Assessing seasonal river-wetland connectivity using remote sensing-based monitoring in tropical environments," by Dylan Irvine, Kaline de Mello and Porni Mollick, which is currently under review at Ecological Indicators. The work used relationships between river stage, rainfall and gap-filled MNDWI to determine (1) annual inundation, (2) seasonal inundation, and (3) connectivity between the Daly River (Australia) and its floodplain wetlands.

==== Datasets ====
Datasets and code presented here include:
- Google Earth Engine (GEE) scripts to obtain the MNDWI time series (GEE_codes.zip)
- An approach to produce catchment-averaged, daily climate (rainfall, temperature, etc.) datasets from the Queensland Government SILO database (ClimateData.zip)
- An additional dataset/ approach to obtain multiple realisations of the climate data for a region (ClimateDataMonteCarlo.zip)
-The flow and river stage data used (FlowData.zip)
- The resulting MNDWI time series for a collection of pixels on key transects that join wetlands/billabongs to the river (MNDWI_Pixel_Datasets.zip)
- A gap-filling process to address issues with cloud cover (KalmanFilterMethod.zip)
- An approach to identify river stages where wetting or drying occurs, and to identify these by water year (Sep-Aug) (ApplyMNDWI_Stage_Threshold.zip)

Folders are set up to be self-contained; however, the various input files were constructed using a combination of Python and Excel. i.e., the approach is demonstrated here, but the files do not necessarily present a pure workflow from raw data to the final analyses (due to the intermediate steps to prepare data files).

==== Manuscript abstract ====
Understanding the timing of river-floodplain wetland connection is critical for anticipating ecological risks, including aquatic fauna strandings. In the wet–dry tropics of northern Australia, these risks may intensify due to climate change and water extraction. We combined Sentinel-2-derived modified normalised difference water index (MNDWI), river stage, and rainfall data to monitor inundation dynamics and connectivity between the Daly River (Australia) and three permanent wetlands that act as refugia for aquatic species. We assess annual flood frequency (2018–2025), monthly inundated area, and their relationships with rainfall and river stage. Data gaps due to cloud cover were gap-filled using a random walk model with Kalman filtering and smoothing. Gap-filled MNDWI enabled the detection of spatiotemporal wetness patterns along transects connecting the wetland to the river. Results reveal large interannual variability in inundation, with 2018–2019 and 2019–2020 exhibiting low persistence and extent of flooding, while 2023–2024 showed widespread and prolonged inundation. Connectivity duration differed among transects(6—112 days). We identify stage thresholds (m) for disconnection as an indicator of river-wetland connectivity, with first disconnection dates varying between February—July, depending on the transect. We also derive three pixel-based hydrological indicators: first wetting day, last drying day, and seasonal duration of wet conditions (days yr⁻¹). The strength of relationships between inundation and predictors supports the use of these readily available datasets for forecasting disconnection timing. We provide a practical approach to inform aquatic biodiversity conservation planning measures that can be readily adapted to other floodplain systems.

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

ABSTRACT:

VFLUX is a program that calculates one-dimensional vertical fluid flow (seepage flux) through saturated porous media, using heat transport equations. It uses temperature time series data measured by multiple temperature sensors in a vertical profile in order to calculate flux at specific times and depths. VFLUX is written as a MATLAB toolbox, a set of functions that run in the MATLAB environment.

Please cite as: (Gordon et al., 2012; Irvine et al., 2015)
- Irvine, DJ, LK Lautz, MA Briggs, RP Gordon, JM McKenzie. 2015. Experimental evaluation of the applicability of phase, amplitude, and combined methods to determine water flux and thermal diffusivity from temperature time series using VFLUX 2. Journal of Hydrology, 531(3):728-737. doi:10.1016/j.jhydrol.2015.10.054.
- Gordon, RP, LK Lautz, MA Briggs, JM McKenzie. 2012. Automated calculation of vertical pore-water flux from field temperature time series using the VFLUX method and computer program. Journal of Hydrology, 420-421:142-158. doi: 10.1016/j.jhydrol.2011.11.053.
- (Two minor typographical errors were recently discovered in the publication Gordon et al. (2012). The definition for H in Equation 12 (p. 147) should be corrected to read “H = Cw/λo”. Also, Equation 9 (p. 147) should read “κe = (λo/C) + β|v|”, where v is the thermal front velocity. Please note that the code of the VFLUX program has always contained the correct forms of these two equations. Thanks go to Chengpeng Lu of Hohai University and Dylan Irvine of Monash University for bringing these typographical errors to our attention.)

VFLUX 1.2.5: The original VFLUX code, with amplitude- and phase-based solutions to determine flux from temperature time series data, as well as signal processing methods, data visualization, sensitivity analysis, and Monte Carlo error analysis modules.

VFLUX 2.0.0: Includes all functionality of the original VFLUX, with the addition of solutions for the “combined” amplitude ratio and phase lag methods (Luce et al., 2013; McCallum et al., 2012). More information can be found in Irvine et al. (2015).

VFLUX requires the Captain Toolbox and the ‘resample’ function from the MATLAB Signal Processing Toolbox. The Captain Toolbox is available for free at http://www.lancs.ac.uk/staff/taylorcj/tdc/download.php. The Signal Processing toolbox is available from MathWorks at http://www.mathworks.com/products/signal/. If the Signal Processing Toolbox is not available to you, then an alternate function may be substituted (for more information, contact the authors).

VFLUX Add-ons
(1) vflux_qar_opt is an add on program for VFLUX2 (v 2.0.0 and greater). vflux_qar_opt has two main applications: 1) to fine tune flux estimates using the benefits of two analytical solutions, and 2) to provide a workflow to assess potential streambed scour. vflux_qar_opt can be run after the main vflux functions, where the user can optimize flux estimates by refining the thermal diffusivity (κe) value that best reproduces the known sensor spacing (Δz). More information can be found in the vflux_qar_opt documentation, which is included with the m-file in the download, and associated manuscript (Irvine et al. 2017).
Irvine, DJ, MA Briggs, I Cartwright, CR Scruggs, LK Lautz. 2017. Improved vertical streambed flux estimation using multiple diurnal temperature methods in series. Groundwater, 55(1): 73-80. doi: 10.1111/gwat.12436