A detailed streamflow and groundwater salinity dataset for Muttama Creek Catchment, NSW, Australia

Vervoort, Rutger Willem; van Ogtrop, Floris; Tambrchi, Mina; Akter, Farzina; Buzacott, Alexander; Lessels, Jason; Moloney, James; Kundu, Dipangkar; Dijkstra, Feike; Bishop, Thomas

doi:https://doi.org/10.5194/essd-2025-105

Preprints

https://doi.org/10.5194/essd-2025-105

Preprints

01 Apr 2025

| 01 Apr 2025

Status: a revised version of this preprint is currently under review for the journal ESSD.

A detailed streamflow and groundwater salinity dataset for Muttama Creek Catchment, NSW, Australia

Rutger Willem Vervoort, Floris van Ogtrop, Mina Tambrchi, Farzina Akter, Alexander Buzacott, Jason Lessels, James Moloney, Dipangkar Kundu, Feike Dijkstra, and Thomas Bishop

Abstract. Dryland salinity remains a major global natural resource management concern, and which is amplified in Australia. However, limited detailed space-time data sets with observations of stream and groundwater salinity has constrained a deep understanding of the range of processes that can lead to dryland salinity problems in landscapes. The aim of this study is to report on the open dataset resulting from a 14-year data collection effort in a subcatchment of the Murrumbidgee catchment in New South Wales, Australia. Over a 14-year period a series of different sampling campaigns has resulted in a large dataset with hydrogeochemical data which includes both in-situ (field) data and post laboratory analysis of major anions and cations. This data is augmented with observed groundwater levels and publicly available streamflow and climate data. The data set covers 23 groundwater sample sites and 39 surface water sites. Because the data was collected by four distinct groups and over many years, we analyse to see if this has caused a bias in the dataset. In addition, we show the major spatial and temporal trends to provide an overview of the dataset. The dataset is made open access to encourage further research and the current paper shows the richness of the collected data and opportunities for further research.

Received: 26 Feb 2025 – Discussion started: 01 Apr 2025

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.

Download & links

Preprint (PDF, 4065 KB)

Supplement (312 KB)

Download & links

Rutger Willem Vervoort, Floris van Ogtrop, Mina Tambrchi, Farzina Akter, Alexander Buzacott, Jason Lessels, James Moloney, Dipangkar Kundu, Feike Dijkstra, and Thomas Bishop

Status: final response (author comments only)

RC1:
'Comment on essd-2025-105', Justin Hughes, 13 Jun 2025

I am pleased to see such a data set being made public. As the authors state, at many of the experimental catchments involving salinity and hydrology, data have not been made public, for various reasons. I do note that while there are many standard methods available for surface water gauging and groundwater sampling, nothing appears available regarding the conjunctive use of manual and logger based groundwater depth measurements, particularly correction, so this paper, while using reasonably common methods, does document these which is pleasing. On a minor technical point, bicarbonate in surface water sourced from groundwater, particularly in the concentrations common in that part of the world, tends to 'gas off' in equilibrium to the atmosphere quite quickly and therefore making bicarbonate concentrations a little more difficult to interpret.

Citation: https://doi.org/10.5194/essd-2025-105-RC1
- AC1: 'Reply on RC1', Willem Vervoort, 29 Jul 2025
  
  Thank you for your support and comments. We have reviewed your comments and will accept your suggestions in the final paper
  
  Citation: https://doi.org/10.5194/essd-2025-105-AC1
RC2:
'Comment on essd-2025-105', Anonymous Referee #2, 14 Jun 2025

This study presents a comprehensive dataset for a catchment with significant ground salinity issue. This data contains long term observation of water flow, ground water and salinity levels, which is very useful for further research. Therefore, I recommend a major revision of the manuscript before it can be accepted for publication.
General comments:
There are so many figures in the main-text. Some of the figures need to be moved to supplementary information.
The authors need to state something about the representativeness of the watershed in the discussion. Otherwise, this is a local dataset. Can you write something about how this dataset can be useful for the study for other regions of the world with similarity in hydrological or geological conditions.
Line-to-line comments:
Figure 4 are not very important; can you move it to supplementary information. I think you only need to present the figures relate to the results.
Figure 5 and 6 can be put together.
Page 8: can you move the Pseudo code to supplementary information
Can you compile figure 10 and 12 together? Also, for Figure 11 and 13.

Line 240: can you compare the items in other national datasets?

Citation: https://doi.org/10.5194/essd-2025-105-RC2
- AC2: 'Reply on RC2', Willem Vervoort, 29 Jul 2025
  
  This study presents a comprehensive dataset for a catchment with significant ground salinity issue. This data contains long term observation of water flow, ground water and salinity levels, which is very useful for further research. Therefore, I recommend a major revision of the manuscript before it can be accepted for publication.
  General comments:
  There are so many figures in the main-text. Some of the figures need to be moved to supplementary information.
  Response: Based on both reviewers’ comments, we agree that we can rationalise and combine some of the figures and reduce the number of figures in the paper.
  The authors need to state something about the representativeness of the watershed in the discussion. Otherwise, this is a local dataset. Can you write something about how this dataset can be useful for the study for other regions of the world with similarity in hydrological or geological conditions.
  Response: We will expand the introduction to highlight this point better.
  The watershed is representative for semi-arid watersheds globally, but typical for Australia where a significant amount of research has taken place at the watershed scale (e.g. Crosbie et al. 2007; Hughes et al. 2007; Hughes et al. 2008; Summerell et al. 2006). Unfortunately, a lot of the older data is not easily accessible and extractable. This paper attempts to correct this by providing an open dataset, which hopefully will also encourage older research to summarise and report open data.
  As all watersheds are unique in some way, it is hard to identify more exact matches to the watershed. However, we believe that the data would be relevant for areas in the US, Canada, Asia and South America (Thorslund and van Vliet, 2020; Stavi et al. 2021).
  Dryland salinity also remains a global problem (Thorslund and van Vliet, 2020; Stavi et al. 2021; McFarlane et al, 2016). In particular the impact of salinity on freshwater systems such as wetlands is recognised as a serious threat (Cañedo-Argüelles et al. 2016). More importantly, in this case it is recognised that not only the EC (such as in the global database from Thorslund and van Vliet (2020)) is of importance, but the actual different chemicals, as they have different impacts on ecology (Cañedo-Argüelles et al. 2016). Our dataset addresses this by providing a long-term database of all major ions as well as salinity values. To strengthen this point, we will add a correlation plot of the EC and the major anions to the paper.
  Finally, there are very few, long term, watershed datasets that include salinity, major ions and cover groundwater and surface water. We hope this data set can help improve our understanding of salinity processes which can then support studies and management of watersheds globally.
  References
  Cañedo-Argüelles, M. et al., 2016. Saving freshwater from salts. Science, 351(6276): 914-916. DOI:doi:10.1126/science.aad3488
  Crosbie, R.S., Hughes, J.D., Friend, J., Baldwin, B.J., 2007. Monitoring the hydrological impact of land use change in a small agricultural catchment affected by dryland salinity in central NSW, Australia. Agricultural Water Management, 88(1–3): 43-53. DOI:http://dx.doi.org/10.1016/j.agwat.2006.08.009
  Hughes, J.D., Crosbie, R.S., van de Ven, R.J., 2008. Salt mobilisation processes from a salinised catchment featuring a perennial stream. Journal of Hydrology, 362(3-4): 308-319.
  Hughes, J.D., Khan, S., Crosbie, R.S., Helliwell, S., Michalk, D.L., 2007. Runoff and solute mobilization processes in a semiarid headwater catchment. Water Resour. Res., 43: W09402. DOI:doi:10.1029/2006WR005465
  McFarlane, D.J., George, R.J., Barrett-Lennard, E.G., Gilfedder, M., 2016. Salinity in Dryland Agricultural Systems: Challenges and Opportunities. In: Farooq, M., Siddique, K.H.M. (Eds.), Innovations in Dryland Agriculture. Springer International Publishing, Cham, pp. 521-547. DOI:10.1007/978-3-319-47928-6_19
  Stavi, I., Thevs, N., Priori, S., 2021. Soil Salinity and Sodicity in Drylands: A Review of Causes, Effects, Monitoring, and Restoration Measures. Frontiers in Environmental Science, Volume 9 - 2021. DOI:10.3389/fenvs.2021.712831
  Summerell, G.K., Tuteja, N.K., Grayson, R.B., Hairsine, P.B., Leaney, F., 2006. Contrasting mechanisms of salt delivery to the stream from three different landforms in South Eastern Australia. Journal of Hydrology, 330(3-4): 681-697.
  Thorslund, J., van Vliet, M.T.H., 2020. A global dataset of surface water and groundwater salinity measurements from 1980–2019. Scientific Data, 7(1): 231. DOI:10.1038/s41597-020-0562-z
  Line-to-line comments:
  Figure 4 are not very important; can you move it to supplementary information. I think you only need to present the figures relate to the results.
  Response: We think highlighting the temporal gaps in the data is important. However, we agree that the current figure is not very informative. We will redo this figure to highlight the data gaps by month and by location.
  Figure 5 and 6 can be put together.
  Response: Agreed, we will combine figure 5 and 6
  Page 8: can you move the Pseudo code to supplementary information
  Response: Agreed, will move this to the supplementary information
  Can you compile figure 10 and 12 together? Also, for Figure 11 and 13.
  Response: We can try to combine Figure 10 & 12, which we originally did, but worried that the maps would become too small. However, we will attempt this again and landscaping the figure. Combining Fig 11 & 13 would probably make the figures too small and the labels very difficult to read. We believe it is therefore better to keep them as separate figures.
  Line 240: can you compare the items in other national datasets?
  Response: Only limited comparisons can be made with existing national datasets. Almost none of the national datasets include sufficient detail in hydrogeochemistry to provide comparison. A lot of the Australian work has concentrated on deep aquifers and the connections with the Murray river and larger regional scales across Australia, which provides limited comparison for catchment level studies of both surface water and groundwater.
  Some comparisons we can be made with the work by Hughes et al. (2007 & 2008) who provides some level of hydrogeochemistry detail for studies in NSW. This suggests that the results in our dataset are similar.
  We will include a comparison to the Australian data in the global dataset from Thorslund and van Vliet (2020) focusing on shallow groundwater and the surface water data in this dataset. However, this dataset only covers EC
  And there is some smaller watershed scale work in the state of Victoria in Australia, but more focussed on groundwater (e.g. Cartwright et al. 2004; Bennetts et al. 2006). Also no detailed data is provided in these papers.
  Bennetts, D.A., Webb, J.A., Stone, D.J.M., Hill, D.M., 2006. Understanding the salinisation process for groundwater in an area of south-eastern Australia, using hydrochemical and isotopic evidence. Journal of Hydrology, 323(1): 178-192. DOI:https://doi.org/10.1016/j.jhydrol.2005.08.023
  Cartwright, I. et al., 2004. Hydrogeochemical and isotopic constraints on the origins of dryland salinity, Murray Basin, Victoria, Australia. Applied Geochemistry, 19(8): 1233-1254. DOI:https://doi.org/10.1016/j.apgeochem.2003.12.006
  Hughes, J.D., Crosbie, R.S., van de Ven, R.J., 2008. Salt mobilisation processes from a salinised catchment featuring a perennial stream. Journal of Hydrology, 362(3-4): 308-319.
  Hughes, J.D., Khan, S., Crosbie, R.S., Helliwell, S., Michalk, D.L., 2007. Runoff and solute mobilization processes in a semiarid headwater catchment. Water Resour. Res., 43: W09402. DOI:doi:10.1029/2006WR005465
  Thorslund, J., van Vliet, M.T.H., 2020. A global dataset of surface water and groundwater salinity measurements from 1980–2019. Scientific Data, 7(1): 231. DOI:10.1038/s41597-020-0562-z
  
  Citation: https://doi.org/10.5194/essd-2025-105-AC2
RC3:
'Comment on essd-2025-105', Anonymous Referee #3, 22 Jun 2025

General Comments
This article offers a brief yet intriguing description of a hydrogeochemical dataset. This dataset comprises 1,160 water samples gathered over a span of 14 years at 62 different sites. Both the dataset and this article hold important value for the community interested in dryland and irrigation salinity. Nevertheless, the article may require substantial revisions prior to publication.
My primary concern lies in the further value of this dataset, considering that the data were collected through inconsistent methods. The authors do provide a comprehensive description of the dataset regarding its collection process, quality, and known and unknown biases. However, the potential of the data set for further use is not adequately emphasized. A concrete example of this critique is Section 3.3. In this section, Table 2 and Figures 8 - 13 present some instances of what this dataset can reveal, but the analysis is rather weak. If six figures and one table only warrant two general paragraphs of explanation, then the necessity of presenting these items might be called into question. Moreover, Figures 10 and 11 are not mentioned at all throughout the article, and Figures 7, 8, and 9 are in different sections from their corresponding references/explanations, indicating that the organization of the content needs considerable improvement.
For a data - description paper, it is preferable to offer readers information beyond just the appearance of the dataset. More importantly, it should inspire others regarding its greater potential. Specific scientific questions that the dataset can contribute to may be helpful.
Details
Line 98 mentions "62 sample locations," which does not align with the sites marked and numbered in Figure 2. Additionally, as the authors have clarified that "not all sites were sampled at all times," I suggest incorporating an extra color bar in Figure 2 for location markers to denote the specific number of samples through different colors. In this case, the previous "GW/SW" color bar could be removed, and the information it conveys could instead be represented by varying shapes of the location markers. Regarding the caption of Figure 2, the mention of "Brown/Orange" seems inaccurate. Isn't the color for "GW" coral red?
Line 109 – 112: The description of Table 1 as well as Table 1 itself is not very clear. Readers cannot tell from Table 1 which locations consist of 6/5/4… variables or at least the numbers of locations that include specific numbers of variables.
L119-L120: The exact instrument information with the data is crucial for others to do research based on data sets not collected by themselves. Could the authors make more effort to supplement the instruments and their configuration information?
L146: ‘pseudocode below’, The comparison operator in the conditional statement of the pseudocode is missing.
L151: ‘due to the a lack of…’
Figures
Some captions of tables/figures, have a comma at the end while others do not, please unify them.
Figure 4: This figure is poorly presented. I cannot distinguish thicker lines from these sparse, thin lines.
Figure 6: Why not use the same visualization method as Figure 5? (with intervals between adjacent bars)

Citation: https://doi.org/10.5194/essd-2025-105-RC3
- AC3:
  'Reply on RC3', Willem Vervoort, 29 Jul 2025
  General Comments
  This article offers a brief yet intriguing description of a hydrogeochemical dataset. This dataset comprises 1,160 water samples gathered over a span of 14 years at 62 different sites. Both the dataset and this article hold important value for the community interested in dryland and irrigation salinity. Nevertheless, the article may require substantial revisions prior to publication.
  My primary concern lies in the further value of this dataset, considering that the data were collected through inconsistent methods. The authors do provide a comprehensive description of the dataset regarding its collection process, quality, and known and unknown biases. However, the potential of the data set for further use is not adequately emphasized. A concrete example of this critique is Section 3.3.
  In this section, Table 2 and Figures 8 - 13 present some instances of what this dataset can reveal, but the analysis is rather weak. If six figures and one table only warrant two general paragraphs of explanation, then the necessity of presenting these items might be called into question. Moreover, Figures 10 and 11 are not mentioned at all throughout the article, and Figures 7, 8, and 9 are in different sections from their corresponding references/explanations, indicating that the organization of the content needs considerable improvement.
  Response: We specifically removed additional analysis of the dataset as this is a “data paper” and we wanted to leave the specific analysis to the users of the datasets. However, given the concerns of the reviewer we can expand section 3.3 to provide a more detailed description of the data and the spatial variation in the data set and how this links to the local geology. In addition, we will include a correlation diagram between the EC and the major ions to highlight the relationships eluded to in the introduction, and we will do a comparison with the Australian EC data for surface water and shallow groundwater from Thorslund and van Vliet (2020).
  We will also check that we describe all the figures in the section in more detail and outline the specific characteristics of the dataset in these figures.
  For a data - description paper, it is preferable to offer readers information beyond just the appearance of the dataset. More importantly, it should inspire others regarding its greater potential. Specific scientific questions that the dataset can contribute to may be helpful.
  Response: See our last comment, we did not want to provide a full analysis of the dataset, as this paper is focussed on simply describing the data and making it available for other researchers. We will also expand the discussion to provide a number of questions that can potentially be answered with the data, some of which are part of our current research:
  Long term trends in the data given climate variation
  
  Spatial and temporal variation in groundwater and surface water interactions
  
  Testing different hydrological and hydro chemical models
  
  Analysing long term trends and changes in a long-term dataset
  
  Details
  Line 98 mentions "62 sample locations," which does not align with the sites marked and numbered in Figure 2. Additionally, as the authors have clarified that "not all sites were sampled at all times," I suggest incorporating an extra color bar in Figure 2 for location markers to denote the specific number of samples through different colors. In this case, the previous "GW/SW" color bar could be removed, and the information it conveys could instead be represented by varying shapes of the location markers. Regarding the caption of Figure 2, the mention of "Brown/Orange" seems inaccurate. Isn't the color for "GW" coral red?
  Response: There are 23 groundwater samples and 39 surface water samples, which equals a total of 62 samples. The numbers are 1 – 23 for the groundwater samples and 1 – 39 for the surface water samples in the data set.
  We agree that the caption of the figure will be corrected and updated to reflect information suggested by this reviewer
  We originally had varying shapes but removed these as the figure became too busy. However, we will now improve the figure by increasing the transparency of the background, and inserting different shapes for groundwater and surface water.
  Line 109 – 112: The description of Table 1 as well as Table 1 itself is not very clear. Readers cannot tell from Table 1 which locations consist of 6/5/4… variables or at least the numbers of locations that include specific numbers of variables.
  Response: We will update the table to provide clearer information about how often and what at each location was sampled.
  L119-L120: The exact instrument information with the data is crucial for others to do research based on data sets not collected by themselves. Could the authors make more effort to supplement the instruments and their configuration information?
  Reponse: Page 6 of the paper already includes the specifics of the instruments, but we will expand this by inserting a table that outlines more specific detail of the probes and the sensors installed on the instruments.
  L146: ‘pseudocode below’, The comparison operator in the conditional statement of the pseudocode is missing.
  Response: Corrected and moved to supplementary material as suggested by the first reviewer
  L151: ‘due to the a lack of…’
  Response: corrected
  Figures
  Some captions of tables/figures, have a comma at the end while others do not, please unify them.
  Response: this will be corrected
  Figure 4: This figure is poorly presented. I cannot distinguish thicker lines from these sparse, thin lines.
  Response: Agreed, we will redo figure 4 to make it clearer. We will group the data by month to make the figure more interpretable and still convey the same information
  Figure 6: Why not use the same visualization method as Figure 5? (with intervals between adjacent bars)
  Response: Agreed, we will redo figure 6
  
  Citation: https://doi.org/10.5194/essd-2025-105-AC3

Rutger Willem Vervoort, Floris van Ogtrop, Mina Tambrchi, Farzina Akter, Alexander Buzacott, Jason Lessels, James Moloney, Dipangkar Kundu, Feike Dijkstra, and Thomas Bishop

Supplement

https://doi.org/10.5194/essd-2025-105-supplement

Data sets

A detailed streamflow and groundwater salinity dataset for Muttama Creek Catchment, NSW, Australia Rutger Willem Vervoort et al. https://doi.org/10.25910/m0wp-8890

Muttama Creek Catchment Groundwater data R. Willem Vervoort and Farzina Akter https://doi.org/10.17605/OSF.IO/BEUWK

Model code and software

MuttamaDataPaper Rutger Willem Vervoort et al. https://github.com/WillemVervoort/MuttamaDataPaper

Rutger Willem Vervoort, Floris van Ogtrop, Mina Tambrchi, Farzina Akter, Alexander Buzacott, Jason Lessels, James Moloney, Dipangkar Kundu, Feike Dijkstra, and Thomas Bishop

Viewed

Total article views: 519 (including HTML, PDF, and XML)

HTML	PDF	XML	Total	Supplement	BibTeX	EndNote
412	82	25	519	22	16	33

HTML: 412
PDF: 82
XML: 25
Total: 519
Supplement: 22
BibTeX: 16
EndNote: 33

Views and downloads (calculated since 01 Apr 2025)

Month	HTML	PDF	XML	Total
Apr 2025	151	22	8	181
May 2025	54	22	1	77
Jun 2025	91	17	8	116
Jul 2025	50	20	8	78
Aug 2025	66	1	0	67

Cumulative views and downloads (calculated since 01 Apr 2025)

Month	HTML	PDF	XML	Total
Apr 2025	151	22	8	181
May 2025	54	22	1	77
Jun 2025	91	17	8	116
Jul 2025	50	20	8	78
Aug 2025	66	1	0	67

Viewed (geographical distribution)

Total article views: 504 (including HTML, PDF, and XML) Thereof 504 with geography defined and 0 with unknown origin.

Country	#	Views	%

Latest update: 24 Aug 2025

Download

Preprint (4065 KB)
Metadata XML

Short summary

A detailed water quality dataset for a 1000 km² catchment in NSW, Australia is presented. The water quality data for surface and groundwater was collected over 14 years and contains more than 1000 records. Despite missing values and different sampling methods and groups, the data provides a consistent overview of the hydrogeochemistry of the catchment. This dataset is valuable for global modelling studies as well as better understanding of local dryland salinity and water quality processes.


Total:	0
HTML:	0
PDF:	0
XML:	0